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WO2019001094A1 - Procédés et dispositifs de codage et de décodage vidéo, et appareil électronique - Google Patents

Procédés et dispositifs de codage et de décodage vidéo, et appareil électronique Download PDF

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Publication number
WO2019001094A1
WO2019001094A1 PCT/CN2018/083229 CN2018083229W WO2019001094A1 WO 2019001094 A1 WO2019001094 A1 WO 2019001094A1 CN 2018083229 W CN2018083229 W CN 2018083229W WO 2019001094 A1 WO2019001094 A1 WO 2019001094A1
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Prior art keywords
distortion
image
preset
distorted
pixel point
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English (en)
Chinese (zh)
Inventor
周璐璐
王莉
武晓阳
浦世亮
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Hangzhou Hikvision Digital Technology Co Ltd
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Hangzhou Hikvision Digital Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/172—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/186—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/80—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation
    • H04N19/82—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation involving filtering within a prediction loop
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
    • H04N19/91—Entropy coding, e.g. variable length coding [VLC] or arithmetic coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N9/00—Details of colour television systems
    • H04N9/64—Circuits for processing colour signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N9/00—Details of colour television systems
    • H04N9/64—Circuits for processing colour signals
    • H04N9/646—Circuits for processing colour signals for image enhancement, e.g. vertical detail restoration, cross-colour elimination, contour correction, chrominance trapping filters

Definitions

  • the present application relates to the field of video codec technology and image processing technology, and in particular, to a video encoding method, and a video decoding method, and related devices and electronic devices.
  • the original video data is subjected to various processing, such as: prediction, transform, quantization, reconstruction, filtering, etc., in which the processed video data may have been pixel-biased relative to the original video data. Shifting, causing visual impairment or artifacts; in addition, in the block-based hybrid coding framework adopted by most video coding systems, because adjacent coding blocks use different coding parameters, such as different transform quantization processes, different prediction methods, Different reference images, etc., the error magnitude and distribution characteristics introduced by each block are independent of each other, and the adjacent block boundaries will produce discontinuities and block effects. These distortions not only affect the subjective and objective quality of the reconstructed image. If the reconstructed image is used as the reference image of the subsequent encoded pixels, it will also affect the prediction accuracy of the subsequent encoded pixels and affect the size of the final video bitstream.
  • various processing such as: prediction, transform, quantization, reconstruction, filtering, etc.
  • a de-distortion filter module is often added after the reconstruction module, as shown in FIG.
  • the traditional de-distortion filter is usually to artificially generalize the characteristics of the distorted image, artificially design the filter structure, and configure the filter coefficients based on experiments and experience, such as the video coding standard H.264 standard, H.265 standard.
  • Deblocking Filter (DF), etc. also has a class of de-distortion filters, which adaptively configure filter structure, filter coefficients, etc. based on some statistical information of local image regions, such as H.265 Sample adaptive offset (SAO) used in the standard, Adaptive Loop Filter (ALF) used in video encoding and decoding, etc., such adaptive filters have high complexity.
  • SAO Sample adaptive offset
  • ALF Adaptive Loop Filter
  • the encoding end needs to write the filter-related parameters that depend on the local statistical information into the code stream to ensure the consistency of the codec, which undoubtedly increases the number of coded bits.
  • deep neural networks can adaptively construct feature descriptions driven by training data, which has higher flexibility and universality.
  • Deep learning has been very successful in many high-level computer vision problems, such as image classification, target detection, and target segmentation.
  • SRCNN super-resolution Convolutional Neural Network
  • CNN Convolutional Neural Network
  • ARCNN Artifacts Reduction Convolutional Neural Network
  • VRCNN variable-filter-size Residue-learning Convolutional Neural Network
  • CNN-based end-to-end image de-distortion algorithm avoids image preprocessing, artificially designing filter coefficients, etc. It automatically learns image distortion features and compensation methods through data driving, which is easier to use, better generalized, and better performance. Obviously, it is especially suitable for the field of image/video compression that combines multiple distortions.
  • the CNN-based loop/post-processing filtering method has a significant improvement in codec performance compared to other loop/post-processing filtering methods, but the computational complexity is caused by the increased algorithm complexity. Increase.
  • An object of the embodiments of the present application is to provide a video encoding method, a decoding method, an apparatus, and an electronic device, to implement a de-distortion processing of a distorted image by using a CNN model different from the related technology in a video encoding and decoding process, and , to achieve a compromise between coding performance and algorithm complexity.
  • the specific technical solutions are as follows:
  • An embodiment of the present application provides a video encoding method, including:
  • the preset decision strategy determine whether to enable the preset de-distortion filtering function
  • the distorted image when determined to be turned on; the distorted image is generated during video encoding of a current original frame image in the video to be encoded;
  • the edge information component representing a distortion feature of the distortion image relative to the current original frame image
  • the CNN model is training based on a preset training set Obtaining that the preset training set includes an original sample image, and a distortion image color component of the plurality of distortion images corresponding to the original sample image, and an edge information component corresponding to each of the distortion images;
  • determining whether to enable the preset de-distortion filtering function according to a preset decision policy includes:
  • the preset de-distortion filtering function When the current original frame image in the video to be encoded is an I frame image, it is determined that the preset de-distortion filtering function is turned on; when the current original frame image in the to-be-coded video is a P-frame image or a B-frame image, it is determined to turn off the preset. Distortion filtering function; or
  • Texture analysis is performed on the reconstructed image; when the analysis result indicates that the texture is flat, it is determined that the preset de-distortion filtering function is turned on; when the analysis result indicates that the texture is complex, it is determined that the preset de-distortion filtering function is turned off, wherein the reconstructed image is in the video to be encoded.
  • the current original frame image is generated during video encoding; or
  • the distorted image is a reconstructed image generated during video encoding of a current original frame image in a video to be encoded;
  • the distortion image is obtained by filtering the reconstructed image by using other preset filtering methods; the reconstructed image is generated during video encoding of the current original frame image in the video to be encoded.
  • the side information component represents at least one of the following distortion features:
  • generating an edge information component corresponding to the distortion image includes:
  • determining a distortion level value of each pixel of the distorted image includes:
  • generating the side information component corresponding to the distortion image by using the obtained distortion degree value of each pixel point based on the position of each pixel point of the distortion image including:
  • the processed distortion level value of each pixel point is determined as the component value of the same position of the pixel point in the side information component corresponding to the distorted image.
  • the embodiment of the present application further provides a video encoding apparatus, including:
  • a first opening determining module configured to determine whether to enable a preset de-distortion filtering function according to a preset decision policy
  • a first image obtaining module configured to obtain a distorted image when the determining is turned on; the distorted image is generated in a process of video encoding the current original frame image in the video to be encoded;
  • a first generating module configured to generate an edge information component corresponding to the distortion image; and the edge information component represents a distortion feature of the distortion image relative to the current original frame image;
  • a first filtering processing module configured to input a distortion image color component of the distortion image and the edge information component into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component;
  • the CNN model And the preset training set includes: an original sample image, and a distortion image color component of the plurality of distortion images corresponding to the original sample image, and an edge information component corresponding to each of the distortion images;
  • An entropy coding module configured to perform entropy coding on the entropy coded data to obtain a coded video bitstream; the to-be entropy coded data is generated by video coding the current original frame image, and includes indicating whether to enable the preset Decision information of the distortion filtering function and side information for generating the side information component.
  • the embodiment of the present application further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus; wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus;
  • a memory for storing a computer program
  • the processor when used to execute a program stored on the memory, implements any of the above video encoding method steps.
  • the embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium stores a computer program, and when the computer program is executed by the processor, implements any of the above video encoding method steps.
  • the embodiment of the present application further provides a computer program, which is implemented by a processor to implement any of the above video encoding method steps.
  • the used CNN model is obtained by training based on a preset training set, where the preset training set includes an original sample image, and a distortion image color of multiple distortion images corresponding to the original sample image.
  • the side information component can represent a distortion characteristic of the distorted image relative to the original sample image; in the process of performing video encoding, the distortion generated in the process of video encoding the current original frame image
  • the image first generates an edge information component corresponding to the distorted image, and then inputs the distorted image color component and the side information component of the distorted image into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component.
  • the distortion image is de-distorted using a CNN model different from the related technology.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a compromise between coding performance and algorithm complexity.
  • An embodiment of the present application provides a video decoding method, including:
  • the distorted image when determined to be turned on; the distorted image is generated during video decoding of the current entropy decoded data;
  • the CNN model is training based on a preset training set Obtained, the preset training set includes an original sample image, and a distorted image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image.
  • determining whether to enable the preset de-distortion filtering function according to the decision information indicating whether the preset de-distortion filtering function is turned on in the current entropy decoding data includes:
  • type information indicating a current original frame image frame type in the current entropy decoded data; when the type information indicates that the current original frame image is an I frame image, determining to start the pre- Setting a distortion filtering function; when the type information indicates that the current original frame image is a P frame image or a B frame image, determining to turn off the preset de-distortion filtering function; or
  • resource information indicating available computing resources of the encoding end; when the resource information indicates that the available computing resources are sufficient, determining to enable the preset de-distortion filtering function; when the resource information indicates that the available computing resources are insufficient When it is determined to turn off the preset de-distortion filtering function; or
  • the distortion image is a reconstructed image generated during video decoding of the current entropy decoded data
  • the distortion image is obtained by filtering the reconstructed image by using another preset filtering method; the reconstructed image is generated during video decoding of the current entropy decoded data.
  • the obtained de-distorted image color component is used for video decoding of subsequent entropy decoded data in the video bitstream;
  • the side information component represents at least one of the following distortion features:
  • generating the side information component corresponding to the distortion image based on the side information included in the current entropy decoding data including:
  • obtaining, from the current entropy decoded data, a distortion level value of each pixel of the distortion image including:
  • generating the side information component corresponding to the distortion image by using the obtained distortion degree value of each pixel point based on the position of each pixel point of the distortion image including:
  • the processed distortion level value of each pixel point is determined as the component value of the same position of the pixel point in the side information component corresponding to the distorted image.
  • the embodiment of the present application further provides a video decoding apparatus, including:
  • An entropy decoding module configured to perform entropy decoding on the received video bitstream to obtain current entropy decoded data
  • a second open determination module configured to determine whether to enable a preset de-distortion filtering function according to the decision information indicating whether to enable the preset de-distortion filtering function in the current entropy decoded data
  • a second image obtaining module configured to obtain a distorted image when the determining is turned on; the distorted image is generated in a process of performing video decoding on the current entropy decoded data;
  • a second generating module configured to generate an edge information component corresponding to the distortion image based on edge information included in the current entropy decoded data; the edge information component indicates that the distortion image corresponds to the current entropy decoded data Distortion characteristics of the original frame image;
  • a second filtering processing module configured to input a distortion image color component of the distortion image and the edge information component into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component; wherein the CNN model
  • the preset training set includes an original sample image, and a distorted image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image.
  • the embodiment of the present application further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus; wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus;
  • a memory for storing a computer program
  • the processor when used to execute a program stored on the memory, implements any of the above video decoding method steps.
  • the embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium stores a computer program, and when the computer program is executed by the processor, implements any of the above video decoding method steps.
  • the embodiment of the present application further provides a computer program, which is implemented by a processor to implement any of the above video decoding method steps.
  • the used CNN model is obtained by training based on a preset training set, where the preset training set includes an original sample image, and a distortion image color of multiple distortion images corresponding to the original sample image. a component, and an edge information component corresponding to each of the distorted images, the side information component can represent a distortion feature of the distorted image relative to the original sample image; in the process of performing video decoding, the current entropy decoded data is obtained from the received video bitstream, and For the distortion image generated in the process of video decoding of the current entropy decoded data, the side information component corresponding to the distorted image is first generated, and then the distorted image color component and the side information component of the distorted image are input into the pre-established CNN model for convolution filtering.
  • the distortion image is de-distorted using a CNN model different from the related art.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a compromise between decoding performance and algorithm complexity.
  • FIG. 1 is a schematic structural diagram of a video coding system in a video coding standard
  • FIG. 2 is a schematic structural diagram of a CNN model provided by an embodiment of the present application.
  • FIG. 3 is a schematic diagram of a data flow of a process of a de-distortion filtering process according to an embodiment of the present disclosure
  • FIG. 4 is a schematic diagram of obtaining a distortion image color component of a distorted image in an embodiment of the present application
  • FIG. 5A is a schematic diagram of side information components in an embodiment of the present application.
  • FIG. 5B is a second schematic diagram of side information components in the embodiment of the present application.
  • FIG. 6 is a flowchart of a video encoding method according to an embodiment of the present application.
  • FIG. 7 is a flowchart of a video encoding method according to an embodiment of the present application.
  • FIG. 8 is a schematic structural diagram of a video encoding system according to an embodiment of the present application.
  • FIG. 9 is a flowchart of a video decoding method according to an embodiment of the present application.
  • FIG. 10 is a flowchart of a video decoding method according to an embodiment of the present application.
  • FIG. 11 is a schematic structural diagram of a video decoding system according to an embodiment of the present application.
  • FIG. 12 is a flowchart of a CNN model training method according to an embodiment of the present application.
  • FIG. 13 is a schematic structural diagram of a video encoding apparatus according to an embodiment of the present disclosure.
  • FIG. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
  • FIG. 15 is a schematic structural diagram of a video decoding apparatus according to an embodiment of the present disclosure.
  • FIG. 16 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
  • the embodiment of the present application provides a video encoding solution.
  • this solution first, according to a preset decision policy, whether to enable the preset de-distortion is determined.
  • the de-distorted image may be used as a reference image for video encoding the subsequent frame image of the current original frame image, and entropy encoding the entropy encoded data to obtain an encoded video bitstream, to be entropy encoded data For video encoding of the current original frame image And to include information indicating whether the preset opening distortion decision information filtering, and for generating side information side information component.
  • the embodiment of the present application further provides a video decoding solution, in which the entropy decoding is performed on the received video bitstream to obtain the current entropy decoded data, and the data is decoded according to the current entropy.
  • the determination is turned on, the distortion image generated during the video decoding of the current entropy decoded data is based on the current entropy.
  • Decoding the side information included in the data generating a corresponding side information component indicating a distortion feature of the original frame image corresponding to the current entropy decoded data, and generating the generated side information component and the distortion image color component of the distortion image
  • the de-distorted image color component is output for generating a de-distorted image, and the de-distorted image can be used as a reference image for the current
  • the image to be played corresponding to the original frame image corresponding to the current entropy decoded data is used for video playback.
  • the CNN model is obtained by training based on a preset training set, and the preset training set includes an original sample image and a plurality of distortion images corresponding to the original sample image.
  • the distorted image color component and the side information component corresponding to each distorted image are obtained by performing multiple iterations according to the preset network structure based on the initialized network parameters.
  • the training set may include an original sample image, and perform image processing in the video encoding and decoding on the original sample image to obtain a plurality of distorted images with different distortion characteristics, thereby obtaining distortion image color components of the plurality of distorted images, and each distortion.
  • the training set may also include a plurality of original sample images, and perform image processing in the above video codec for each original sample image, respectively, to obtain a plurality of distorted images with different distortion characteristics, thereby obtaining distortion image color components of the plurality of distorted images, And side information components corresponding to each of the distorted images.
  • the side information component representing the distorted feature of the distorted image relative to the original image is used as the input of the model, that is, the image capable of representing the distorted image is introduced.
  • Distortion feature information and through model training and practical application experiments for various side information components, it is determined that when using some specific side information components, it can train a CNN model with stronger generalization ability, that is, on average In the sense, the de-distortion ability of a large number of distorted images will be stronger, and thus the distortion image can be better de-distorted to obtain a de-distorted image color component closer to the original image color component.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a folding between codec performance and algorithm complexity. in.
  • the related art proposes separately training a set of network parameters for images of different degrees of distortion, and loading a corresponding set of network parameters according to the degree of distortion of the image in the current application in actual use.
  • a large number of parameters waste storage space, and frequent loading of parameters in an application is an unnecessary waste of computing resources.
  • the degree of distortion of the distorted image relative to the original image can be generated.
  • the side information component, the preset training set may include a plurality of distorted images of different degrees of distortion, and the training of the CNN model and the de-distorting filtering of the distorted image using the trained CNN model are introduced to accurately represent the distorted image.
  • the information of the degree of distortion can be used to train the CNN model of the distorted image adapted to various degrees of distortion, so that a distorted effect can be obtained by using only one set of network parameters for the distorted image with different degrees of distortion.
  • FIG. 2 is a schematic structural diagram of a CNN model implementing the above solution, including: an edge information component generating module 21, a CNN 22, and a network training module 23;
  • CNN 22 can include the following three-layer structure:
  • the input layer processing unit 221 is configured to receive an input of the CNN, where the distortion image color component of the distorted image is included in the solution, and the side information component of the distorted image; and the input data is subjected to a convolution filtering process of the first layer;
  • the hidden layer processing unit 222 performs at least one layer of convolution filtering processing on the output data of the input layer processing unit 221.
  • the output layer processing unit 223 performs convolution filtering processing on the output data of the hidden layer processing unit 222, and outputs the result as a de-distorted image color component for generating a de-distorted image.
  • FIG. 3 is a schematic diagram of a data flow for implementing a de-distortion filtering process flow in the above solution, wherein a distorted image color component of the distorted image and an edge information component of the distorted image are input as input data into a pre-trained CNN model,
  • the CNN model can be represented by the CNN of the preset structure and the configured network parameter set, and the input data is subjected to convolution filtering processing of the input layer, the hidden layer and the output layer to obtain de-distorted image data.
  • the input data as the CNN model may include one or more side information components according to actual needs, and may also include one or more distorted image color components, for example, including at least One of the R color component, the G color component, and the B color component, correspondingly, includes one or more de-distorted image color components.
  • the process of performing video codec there may be distortion only for one of the color components, and only the color component of the distorted image may be used as input data during de-distortion processing, such as two types. If there is distortion in the color component, the two color components of the distorted image are taken as input data, and correspondingly, the corresponding de-distorted image color component is output.
  • the stored data of each pixel of an image including the values of all the color components of the pixel, in the embodiment of the present application, when obtaining the distorted image color component of the distorted image, the storage from each pixel may be needed as needed.
  • the values of the desired one or more color components are extracted to obtain a distorted image color component of the distorted image.
  • the value of the R color component of each pixel is extracted therefrom, thereby obtaining the R color component of the distorted image.
  • the side information component which represents the distortion feature of the distorted image relative to the original image
  • the side information component is an expression of the distorted feature determined by the image processing process.
  • the above distortion feature may include at least one of the following distortion features:
  • the side information component can represent the degree of distortion of the distorted image relative to the original image.
  • the side information component can also represent the distorted position of the distorted image relative to the original image.
  • the image is usually divided into a plurality of non-overlapping and non-fixed coding units, and the coding unit performs predictive coding separately.
  • the distortion between coding units is usually not consistent, and pixel mutations usually occur at the boundary of the coding unit. Therefore, the boundary coordinates of the coding unit can be used as a priori edge to represent the distortion position. information.
  • the side information component can also represent the distortion type of the distorted image relative to the original image.
  • different coding units in the image may adopt different prediction modes, and different prediction modes may affect the distribution of residual data, thereby affecting distortion.
  • the characteristics of the image, therefore, the prediction mode of the coding unit can be used as an edge information that characterizes the type of distortion.
  • the side information component may be a combination of one or more of the foregoing, or may be a plurality of side information components of the foregoing one, for example, in the video encoding and decoding process, after image processing.
  • the distortion degree of the distorted image may be represented by a parameter of physical meaning, or the distortion degree of the distorted image may be represented by two parameters of different physical meanings. Accordingly, one or more representations may be distorted according to actual needs.
  • the degree of side information components are used as input data for the CNN model.
  • the matrix structure of the side information component is the same as the matrix structure of the distorted image color component, wherein the coordinates [0, 0], [0, 1] represent the distortion position, and the element value 1 of the matrix represents the degree of distortion, that is,
  • the side information component can simultaneously indicate the degree of distortion and the position of the distortion.
  • the coordinates [0, 0], [0, 1], [2, 0], [2, 4] represent the distortion position
  • the element values 1 and 2 of the matrix represent the distortion type, that is, the side information component. At the same time, it can indicate the type of distortion and the position of distortion.
  • two side information components respectively illustrated in FIG. 5A and FIG. 5B may be simultaneously included.
  • the side information component may include side information components respectively corresponding to each of the distorted image color components.
  • FIG. 6 a video coding method is provided, as shown in FIG. 6, which specifically includes the following processing steps:
  • Step 61 Determine, according to a preset decision policy, whether to enable the preset de-distortion filtering function.
  • Step 62 When it is determined to be turned on, a distorted image is obtained, and the distorted image is generated during video encoding of the current original frame image in the video to be encoded.
  • Step 63 Generate an edge information component corresponding to the distortion image.
  • the side information component represents a distortion feature of the distorted image relative to the current original frame image, and is an expression of the distortion feature determined by the image processing process.
  • Step 64 Input the distortion image color component of the distorted image and the generated side information component into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component.
  • the CNN model is obtained by training based on a preset training set, where the preset training set includes an original sample image, and a distortion image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image. .
  • Step 65 Entropy coding the entropy coded data to obtain a coded video bitstream.
  • the data to be entropy encoded is generated by video encoding the current original frame image, and includes decision information indicating whether to enable the preset de-distortion filtering function, and side information for generating the side information component.
  • the video encoding method will be described in detail below by taking an example of the degree of distortion of the distorted image relative to the original image by using the side information component as an example.
  • FIG. 7 is a flowchart of a video encoding method according to an embodiment of the present application, which specifically includes the following processing steps:
  • Step 71 Determine, according to a preset decision policy, whether to enable the preset de-distortion filtering function.
  • the following decision strategy may be used to determine whether to enable the preset de-distortion filtering function:
  • the first decision strategy when the current original frame image in the video to be encoded is an I frame image, it is determined to enable the preset de-distortion filtering function; when the current original frame image in the to-be-coded video is a P-frame image or a B-frame image , determine to turn off the preset de-distortion filtering function.
  • the second decision strategy texture analysis of the reconstructed image; when the analysis result indicates that the texture is flat, it is determined that the preset de-distortion filtering function is turned on; when the analysis result indicates that the texture is complex, it is determined that the preset de-distortion filtering function is turned off; wherein, the reconstructed image is Generated during the process of video encoding the current original frame image in the encoded video.
  • the reconstructed image may be subjected to texture analysis to obtain a feature parameter representing the image texture feature, and the feature parameter is compared with a preset parameter threshold, which is greater than a preset parameter threshold, indicating that the texture is flat, not greater than a preset parameter threshold, indicating The texture is complicated.
  • the gray level co-occurrence matrix of the reconstructed image is first determined, and the feature parameter energy is extracted from the gray level co-occurrence matrix, and the energy of the gray level co-occurrence matrix can describe the distribution of the gray space uniformity of the reconstructed image and the reconstructed image.
  • the overall complexity When the element distribution in the gray level co-occurrence matrix is concentrated near the main diagonal, the energy value is correspondingly larger, indicating that the gray distribution of the image is relatively uniform, and the texture is complex from the overall view; otherwise, the texture is flat.
  • the gray level co-occurrence matrix of an image and the method for determining the energy of the gray level co-occurrence matrix, various methods can be employed, and detailed descriptions are not given herein.
  • the third decision strategy when the available computing resources are sufficient, determining to enable the preset de-distortion filtering function; when the available computing resources are insufficient, determining to turn off the preset de-distortion filtering function; wherein, the available computing resources may be the method for performing the video encoding The available computing resources for the encoding device.
  • the number of available computing resources when the number of available computing resources does not reach the preset resource threshold, it indicates that the available computing resources are sufficient. When the number of resources reaches the preset resource threshold, it indicates that the available computing resources are insufficient.
  • switch signaling indicating whether to turn on or off may be generated. For example, if it is determined that the preset de-distortion filtering function is enabled, the switch signaling is set to the first value; and when the preset de-distortion filtering function is turned off, the switch signaling is set to the second value. As shown in FIG. 8, the switch signaling can be input to the switch module between the reconstruction module and the CNN module to control the opening and closing of the de-distortion filtering function accordingly.
  • Step 72 When it is determined to be turned on, a distorted image is obtained, which is generated during video encoding of the current original frame image in the video to be encoded.
  • the reference image for video encoding the subsequent frame image may also be subjected to inverse quantization, inverse transform, reconstruction, and de-distortion filtering.
  • the distortion image in this step may be a reconstructed image generated after the reconstruction process in the process, or may be obtained by filtering the reconstructed image by using other preset filtering methods.
  • other preset filtering methods may be DF filtering. method.
  • Step 73 Determine, for the distortion image to be processed, a distortion degree value of each pixel of the distortion image.
  • the physical parameter indicating the degree of distortion may also be different.
  • the corresponding degree of distortion capable of accurately indicating the degree of distortion of the pixel may be determined as follows. value:
  • the quantization parameters of each coding region are known, that is, the quantization parameter of each coding region can be obtained, and the quantization parameter of the coding region where each pixel of the distortion image is located is determined as The degree of distortion of each pixel of the distorted image.
  • Step 74 Generate an edge information component corresponding to the distortion image by using the obtained distortion degree value of each pixel point based on the position of each pixel point of the distortion image.
  • each component value included in the side information component corresponds to a pixel point at the same position on the distorted image.
  • the side information component Since each component value included in the side information component corresponds to a pixel point of the same position on the distorted image, the side information component has the same structure as the distorted image color component of the distorted image, that is, the matrix representing the side information component and the color of the distorted image.
  • the matrix of components is of the same type.
  • the acquired distortion level value of each pixel point may be determined as the component value of the same position of the pixel point in the side information component corresponding to the distortion image, that is, directly
  • the distortion degree value of the pixel is determined as the component value corresponding to the pixel.
  • the acquired distortion degree value of each pixel point may be normalized based on the pixel value range of the distorted image to obtain the degree of distortion after processing.
  • the range of distortion values after processing is the same as the range of pixel values;
  • the processed distortion level value of each pixel point is determined as the component value of the same position of the pixel point in the side information component corresponding to the distorted image.
  • the distortion degree value of the pixel point can be standardized by the following formula:
  • norm(x) is the processed distortion degree value obtained after normalization processing
  • x is the distortion degree value of the pixel point
  • the pixel value range of the distorted image is [PIXEL MIN , PIXEL MAX ]
  • the distortion degree value of the pixel point is taken.
  • the range of values is [QP MIN , QP MAX ].
  • the side information component of the distorted image is generated, and the process of generating the side information component is also understood to be that the side information guide map corresponding to the distorted image is generated, and the side information guide map passes the side information component thereof.
  • the scheme is described by taking the structure of the CNN model including the input layer, the hidden layer, and the output layer as an example.
  • Step 75 The distortion image color component of the distorted image and the generated side information component are used as input data of the pre-established CNN model, and the first layer is subjected to convolution filtering processing by the input layer, which may be specifically as follows:
  • input data can be input to the network through respective channels.
  • the distorted image color component Y of the c y channel and the side information component M of the c m channel can be combined in the channel dimension.
  • the input data I of the c y +c m channel together form the input data I of the c y +c m channel, and perform multidimensional convolution filtering and nonlinear mapping on the input data I by using the following formula to generate n 1 image blocks represented in a sparse form:
  • F 1 (I) is the output of the input layer
  • I is the input of the convolution layer in the input layer
  • * is the convolution operation
  • W 1 is the weight coefficient of the convolution layer filter bank of the input layer
  • B 1 is the input
  • g() is a nonlinear mapping function.
  • W 1 corresponds to n 1 convolution filters, that is, n 1 convolution filters are applied to the input of the convolution layer of the input layer, and n 1 image blocks are output; convolution of each convolution filter
  • the size of the kernel is c 1 ⁇ f 1 ⁇ f 1 , c 1 is the number of input channels, and f 1 is the spatial size of each convolution kernel.
  • Step 76 The hidden layer performs further high-dimensional mapping on the image block F 1 (I) of the sparse representation of the input layer output.
  • the convolution layer number, the convolution layer connection mode, the convolution layer attribute, and the like included in the hidden layer are not limited, and various structures known at present may be adopted, but the hidden layer includes at least 1 convolution layer.
  • the hidden layer contains a N-1 (N ⁇ 2) layer convolutional layer, and the hidden layer processing is represented by:
  • F i (I) g(W i *F i-1 (I)+B i ), i ⁇ 2,3,...,N ⁇ ;
  • F i (I) represents the output of the i-th layer convolutional layer in the CNN
  • * is the convolution operation
  • W i is the weight coefficient of the i-th layer convolutional layer filter bank
  • B i is the convolution layer filter bank
  • the offset coefficient, g() is a nonlinear mapping function.
  • W i corresponds to n i convolution filters, that is, n i convolution filters are applied to the input of the i-th convolution layer, and n i image blocks are output; convolution of each convolution filter
  • the size of the kernel is c i ⁇ f i ⁇ f i , c i is the number of input channels, and f i is the spatial size of each convolution kernel.
  • g() the convolution processing expression of the hidden layer in this embodiment is:
  • F 2 (I) max (0, W 2 * F 1 (I) + B 2 ).
  • Step 77 The output layer aggregates the high-dimensional image block F N (I) output by the hidden layer, and outputs the de-distorted image color component.
  • the structure of the output layer is not limited in the embodiment of the present application, and the output layer may be a Residual Learning structure, a Direct Learning structure, or other structures.
  • the processing using the Residual Learning structure is as follows:
  • the convolution operation is performed on the output of the hidden layer to obtain the compensation residual, and then added to the input distortion image color component to obtain the de-distorted image color component, that is, the de-distorted image is obtained.
  • the output layer processing can be expressed by the following formula:
  • F(I) is the output of the output layer
  • F N (I) is the output of the hidden layer
  • * is the convolution operation
  • W N+1 is the weight coefficient of the convolutional layer filter bank of the output layer
  • B N+ 1 is an offset coefficient of a convolution layer filter bank of the output layer
  • Y is a distorted image color component to be subjected to de-distortion processing without undergoing convolution filtering processing.
  • W N+1 corresponds to n N+1 convolution filters, that is, n N+1 convolution filters are applied to the input of the N+1th convolution layer, and n N+1 image blocks are output.
  • n N+1 is the number of output de-distorted image color components, generally equal to the number of input distortion image color components. If only one de-distorted image color component is output, n N+1 is generally 1
  • the size of the convolution kernel of each convolution filter is c N+1 ⁇ f N+1 ⁇ f N+1
  • c N+1 is the number of input channels
  • f N+1 is the space of each convolution kernel The size on the top.
  • the de-distorted image color component is directly output, that is, the de-distorted image is obtained.
  • the output layer processing can be expressed by the following formula:
  • F(I) is the output of the output layer
  • F N (I) is the output of the hidden layer
  • * is the convolution operation
  • W N+1 is the weight coefficient of the convolutional layer filter bank of the output layer
  • B N+ 1 is the offset coefficient of the convolution layer filter bank of the output layer.
  • W N+1 corresponds to n N+1 convolution filters, that is, n N+1 convolution filters are applied to the input of the N+1th convolution layer, and n N+1 image blocks are output.
  • n N+1 is the number of output de-distorted image color components, generally equal to the number of input distortion image color components. If only one de-distorted image color component is output, n N+1 is generally 1
  • the size of the convolution kernel of each convolution filter is c N+1 ⁇ f N+1 ⁇ f N+1
  • c N+1 is the number of input channels
  • f N+1 is the space of each convolution kernel The size on the top.
  • the output layer adopts a Residual Learning structure, and the output layer includes a convolution layer.
  • the convolution processing expression of the output layer in this embodiment is:
  • step 78 when the data obtained by encoding the video needs to be transmitted to the decoding end, the following step 78 may be further included.
  • Step 78 Entropy coding the entropy coded data to obtain a coded video bitstream.
  • the data to be entropy encoded is generated by video encoding the current original frame image, and includes decision information about whether to enable the preset de-distortion filtering function, and side information for generating the side information component.
  • the decision information may be information indicating whether the preset de-distortion filtering function is directly enabled, and the decision information is explicit, that is, the value of the switch signaling may be directly indicated, for example, similar to the foregoing switch signaling.
  • the value is set to be on when the decision information is the first value, and is off when the decision information is the second value.
  • the decision information may also be information indicating indirectly whether to enable the preset de-distortion filtering function, and the decision information is implicit, that is, the value of the switch signaling may be derived by pushing, for example, for the first decision above.
  • the policy the decision information may be type information indicating the frame type of the original frame image.
  • the decision information may be resource information indicating available computing resources.
  • the decision information is the information carried in the video bitstream in the video coding standard
  • the video bit stream generated in the related video coding and decoding standard is not added to the bit stream in the embodiment.
  • the decision information may be added to the entropy encoded data as a type of information required for video decoding, and the decision information is added.
  • the entropy-encoded data is entropy encoded to obtain an encoded video bitstream, so that the decoding end can obtain the decision information from the video bitstream for determining whether to enable the preset de-distortion filtering function.
  • the side information used to generate the side information component is the information carried in the video bit stream in the video encoding and decoding standard
  • the video bit stream generated in the video coding and decoding standard is compared. No extra bit data will be added.
  • the quantization parameter of the coding region where each pixel point of the distorted image is used is used as the side information
  • the video bit stream generated in the related video codec standard carries the quantization parameter, in this step, according to the related video.
  • the codec standard generates a video bitstream.
  • the side information used to generate the side information component does not exist in the video bit stream generated in the related video codec standard, the side information may be added to the entropy coded data as a type of information required for video decoding.
  • the entropy-encoded data to which the side information is added is entropy encoded to obtain a coded video bitstream, so that the decoding end can obtain the side information from the video bitstream for video decoding processing on the decoding end.
  • the reconstruction module is directly connected to the reference image buffer, that is, the reconstructed image is used as a reference image, and may be reconstructed.
  • the module is connected to other de-distortion filtering modules, and is filtered by other filtering methods, and the obtained image is used as a reference image.
  • the embodiment of the present application further provides a video decoding method, as shown in FIG. 9, which may include the following steps:
  • Step 91 Entropy decoding the received video bitstream to obtain current entropy decoded data.
  • Step 92 Determine whether to enable the preset de-distortion filtering function according to the decision information indicating whether to enable the preset de-distortion filtering function in the current entropy decoded data.
  • Step 93 When it is determined to be turned on, a distorted image is obtained, which is generated during video decoding of the current entropy decoded data.
  • Step 94 Generate an edge information component corresponding to the distortion image based on the side information included in the current entropy decoded data, where the edge information component represents a distortion feature of the original frame image corresponding to the current entropy decoded data.
  • Step 95 Input the distortion image color component of the distortion image and the side information component into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component.
  • the CNN model is obtained by training based on a preset training set, where the preset training set includes an original sample image, and a distortion image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image. .
  • the distortion image is de-distorted using a CNN model different from the related art.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a compromise between decoding performance and algorithm complexity.
  • the video decoding method will be described in detail below by taking the side information component as the example of the degree of distortion of the original frame image corresponding to the current entropy decoded data.
  • FIG. 10 is a flowchart of a video decoding method according to an embodiment of the present disclosure, which specifically includes the following processing steps:
  • Step 101 The decoding end performs entropy decoding on the received video bitstream to obtain current entropy decoded data.
  • the obtained current entropy decoded data corresponds to one frame image in the original video, and based on the current entropy decoded data, the decoding process is performed, and the decoded image corresponding to the frame image can be obtained, so as to facilitate the description of the technical solution,
  • This frame image is referred to as the original frame image corresponding to the current entropy decoded data.
  • Step 102 Determine, according to the decision information indicating whether to enable the preset de-distortion filtering function in the current entropy decoded data, whether to enable the preset de-distortion filtering function.
  • the decision information directly indicates whether the information of the preset de-distortion filtering function is enabled
  • the decision information is explicit, that is, the value of the decision information can be directly used as the value of the switching signaling.
  • the decision information when the decision information is the first value, it indicates that the value is set to be the first value; when the decision information is the second value, it indicates that the value is off, and the value of the switch signaling is set. The second value.
  • the switch signaling can be input to the switch module between the reconstruction module and the CNN module to control the opening and closing of the de-distortion filtering function accordingly.
  • the decision information indirectly indicates whether the information of the preset de-distortion filtering function is enabled, the decision information is implicit, that is, the value of the switch signaling can be obtained by pushing, for example, the following three can be adopted. Ways:
  • the first mode obtaining type information indicating a current original frame image frame type in the current entropy decoded data from the current entropy decoded data; and determining to enable the preset de-distortion when the type information indicates that the current original frame image is an I frame image
  • the filtering function when the type information indicates that the current original frame image is a P frame image or a B frame image, it is determined to turn off the preset de-distortion filtering function.
  • the second method video decoding the current entropy decoded data, obtaining a reconstructed image, performing texture analysis on the reconstructed image; when the analysis result indicates that the texture is flat, determining to enable the preset de-distortion filtering function; when the analysis result indicates that the texture is complex, the determination is closed. Pre-distortion filtering function.
  • the reconstructed image may be subjected to texture analysis to obtain a feature parameter representing the image texture feature, and the feature parameter is compared with a preset parameter threshold, which is greater than a preset parameter threshold, indicating that the texture is flat, not greater than a preset parameter threshold, indicating The texture is complicated.
  • the gray level co-occurrence matrix of the reconstructed image is first determined, and the feature parameter energy is extracted from the gray level co-occurrence matrix, and the energy of the gray level co-occurrence matrix can describe the distribution of the gray space uniformity of the reconstructed image and the reconstructed image.
  • the overall complexity When the element distribution in the gray level co-occurrence matrix is concentrated near the main diagonal, the energy value is correspondingly larger, indicating that the gray distribution of the image is relatively uniform, and the texture is complex from the overall view; otherwise, the texture is flat.
  • the gray level co-occurrence matrix of an image and the method for determining the energy of the gray level co-occurrence matrix, various methods can be employed, and detailed descriptions are not given herein.
  • the third mode obtaining resource information indicating available computing resources of the encoding end from the current entropy decoding data; determining that the preset de-distortion filtering function is enabled when the resource information indicates that the available computing resources are sufficient; and when the resource information indicates that the available computing resources are insufficient , determine to turn off the preset de-distortion filtering function.
  • the number of available computing resources when the number of available computing resources does not reach the preset resource threshold, it indicates that the available computing resources are sufficient. When the number of resources reaches the preset resource threshold, it indicates that the available computing resources are insufficient.
  • the switch signaling can be input to the switch module between the reconstruction module and the CNN module to control the opening and closing of the de-distortion filtering function accordingly.
  • Step 103 When it is determined to be turned on, a distorted image is obtained, which is generated during video decoding of the current entropy decoded data.
  • the current entropy decoded data obtained by entropy decoding the video bitstream includes residual data
  • the process of video decoding the current entropy decoded data includes inverse quantization and inverse transform of the residual data. Reconstruction and de-aliasing filtering processing.
  • the distortion image in this step may be a reconstructed image generated after the reconstruction process in the process, or may be obtained by filtering the reconstructed image by using other preset filtering methods.
  • other preset filtering methods may be DF filtering. method.
  • Step 104 Obtain edge information of the distorted image from the current entropy decoded data, and specifically may obtain a distortion level value of each pixel of the distorted image.
  • the physical parameters indicating the degree of distortion may also be different.
  • the corresponding degree of distortion capable of accurately representing the degree of distortion of the pixel may be determined as follows. value:
  • the quantization parameter of each coding region of the original frame image corresponding to the current entropy decoded data is obtained, and the quantization parameter of the coding region where each pixel point of the distortion image is located is Determine the value of the distortion level for this pixel.
  • Step 105 Generate an edge information component corresponding to the distortion image by using the obtained distortion degree value of each pixel point based on the position of each pixel point of the distortion image.
  • each component value included in the side information component corresponds to a pixel point at the same position on the distorted image.
  • the side information component Since each component value included in the side information component corresponds to a pixel point of the same position on the distorted image, the side information component has the same structure as the distorted image color component of the distorted image, that is, the matrix representing the side information component and the color of the distorted image.
  • the matrix of components is of the same type.
  • the acquired distortion level value of each pixel point may be determined as the component value of the same position of the pixel point in the side information component corresponding to the distortion image, that is, directly
  • the distortion degree value of the pixel is determined as the component value corresponding to the pixel.
  • the acquired distortion degree value of each pixel point may be normalized based on the pixel value range of the distorted image to obtain the degree of distortion after processing.
  • the range of distortion values after processing is the same as the range of pixel values;
  • the processed distortion level value of each pixel point is determined as the component value of the same position of the pixel point in the side information component corresponding to the distorted image.
  • the distortion degree value of the pixel point can be standardized by the following formula:
  • norm(x) is the processed distortion degree value obtained after normalization processing
  • x is the distortion degree value of the pixel point
  • the pixel value range of the distorted image is [PIXEL MIN , PIXEL MAX ]
  • the distortion degree value of the pixel point is taken.
  • the range of values is [QP MIN , QP MAX ].
  • the side information component of the distorted image is generated, and the process of generating the side information component is also understood to be that the side information guide map corresponding to the distorted image is generated, and the side information guide map passes the side information component thereof.
  • the scheme is described by taking the structure of the CNN model including the input layer, the hidden layer, and the output layer as an example.
  • Step 106 The distortion image color component of the distorted image and the generated side information component are used as input data of a pre-established CNN model, and the first layer of convolution filtering processing is performed by the input layer.
  • This step may be the same as step 75 in FIG. 7 above, and will not be described in detail herein.
  • Step 107 The hidden layer performs further high-dimensional mapping on the image block F 1 (I) of the sparse representation outputted by the input layer.
  • This step may be the same as step 76 in FIG. 7 above, and will not be described in detail herein.
  • Step 108 The output layer aggregates the high-dimensional image block F N (I) output by the hidden layer, and outputs the de-distorted image color component.
  • This step may be the same as step 77 in FIG. 7 above, and will not be described in detail herein.
  • the obtained de-distorted image color component may be used for video decoding of subsequent entropy decoded data in the video bitstream; or may be used to generate the original corresponding to the current entropy decoded data.
  • the decoded image corresponding to the frame image serves as a frame image to be played.
  • the reconstruction module may be directly connected to the reference image buffer, that is, the reconstructed image is used as a reference image, and may be reconstructed.
  • the module is connected to other de-distortion filtering modules, and is filtered by other filtering methods, and the obtained image is used as a reference image.
  • the pre-established CNN model is used to perform de-distortion filtering processing on the distorted image.
  • the CNN model used for video encoding and video decoding can be the same.
  • the embodiment of the present application further provides a CNN model training method, as shown in FIG. 12, which specifically includes the following processing steps:
  • Step 121 Acquire a preset training set.
  • the preset training set includes an original sample image, a distorted image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image, and the side information component corresponding to the distorted image indicates that the distorted image is relatively original. Distortion characteristics of the sample image. The distortion characteristics of the plurality of distorted images are different.
  • the original sample image (ie, the undistorted natural image) may be subjected to an image processing of different degrees of distortion to obtain respective corresponding distortion images, and according to the steps in the above-described de-distortion method, for each distortion
  • the image generates corresponding side information components, so that each original sample image, the corresponding distortion image, and the corresponding side information component form an image pair, and the image pairs constitute a preset training set ⁇ .
  • the training set may include an original sample image, and the image processing is performed on the original sample image to obtain a plurality of distortion images having different distortion characteristics, and side information components corresponding to each of the distortion images;
  • the training set may also include a plurality of original sample images, and the image processing is performed separately for each of the original sample images to obtain a plurality of distorted images having different distortion characteristics, and side information components corresponding to each of the distorted images.
  • Step 122 Initialize parameters of the network parameter set of the CNN for the CNN of the preset structure.
  • the initialized parameter set may be represented by ⁇ 1 , and the initialized parameters may be set according to actual needs and experience.
  • the high-level parameters related to the training such as the learning rate and the gradient descent algorithm, may be appropriately set, and various manners in the related art may be used, and detailed descriptions are not provided herein.
  • Step 123 Perform forward calculation, as follows:
  • the distortion image color component of each of the distortion images in the preset training set and the corresponding side information component are input to the CNN of the preset structure for convolution filtering processing, and the de-distorted image color component corresponding to the distortion image is obtained.
  • the forward calculation of the CNN of the parameter set ⁇ i is performed on the preset training set ⁇ , and the output F(Y) of the CNN, that is, the de-distorted image color component corresponding to each distortion image is obtained.
  • the current parameter set is ⁇ 1 .
  • the current parameter set ⁇ i is obtained by adjusting the parameter set ⁇ i-1 used last time. description.
  • Step 124 Determine a loss value of the de-distorted image based on the original image color component of the plurality of original sample images and the obtained de-distorted image color component.
  • MSE Mean Squared Error
  • H represents the number of pairs of images selected from the preset training set in a single training
  • I h represents the input data of the combined edge component and the distorted image color component corresponding to the hth distorted image
  • ⁇ i ) represents the de-distorted image color component calculated by the CNN forwardly in the parameter set ⁇ i for the h-th distorted image
  • X h represents the original image color component corresponding to the h-th distorted image
  • i is the current pre-existing Counts the number of calculations.
  • Step 125 Determine, according to the loss value, whether the CNN of the preset structure adopting the current parameter set converges; if not, go to step 126; if it converges, go to step 127.
  • convergence when the loss value is less than the preset loss value threshold, convergence may be determined; or when the difference between the loss value and the previous calculated loss value is less than the preset change threshold, the convergence is determined, and the application is implemented.
  • the example is not limited here.
  • step 126 the parameters in the current parameter set are adjusted to obtain an adjusted parameter set, and then proceeds to step 123 for the next forward calculation.
  • the back propagation algorithm can be used to adjust the parameters in the current parameter set.
  • Step 127 The current parameter set is used as the final parameter set of the output ⁇ final , and the CNN of the preset structure adopting the final parameter set ⁇ final is used as the trained CNN model.
  • another embodiment of the present application further provides a video encoding apparatus, as shown in FIG.
  • the first open determination module 131 is configured to determine whether to enable the preset de-distortion filtering function according to the preset decision policy;
  • a first image obtaining module 132 configured to obtain a distorted image when the determining is turned on; the distorted image is generated in a process of video encoding the current original frame image in the video to be encoded;
  • a first generation module 133 configured to generate an edge information component corresponding to the distortion image; and an edge information component represents a distortion feature of the distortion image relative to the current original frame image;
  • the first filter processing module 134 is configured to input the distortion image color component and the side information component of the distorted image into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component; wherein the CNN model is based on preset training.
  • the preset training set includes the original sample image, the distorted image color component of the plurality of distorted images corresponding to the original sample image, and the side information component corresponding to each distorted image;
  • the entropy coding module 135 is configured to perform entropy coding on the entropy coded data to obtain a coded video bitstream.
  • the entropy coded data is generated by video coding the current original frame image, and includes indicating whether to enable the preset de-distortion filtering function. Decision information, and side information for generating side information components.
  • the first open determination module 131 may be specifically configured to:
  • the preset de-distortion filtering function When the current original frame image in the video to be encoded is an I frame image, it is determined that the preset de-distortion filtering function is turned on; when the current original frame image in the to-be-coded video is a P-frame image or a B-frame image, it is determined to turn off the preset. Distortion filtering function; or;
  • Texture analysis is performed on the reconstructed image; when the analysis result indicates that the texture is flat, it is determined that the preset de-distortion filtering function is turned on; when the analysis result indicates that the texture is complex, it is determined that the preset de-distortion filtering function is turned off; wherein the reconstructed image is the current in the video to be encoded
  • the original frame image is generated during video encoding; or
  • the distorted image may be a reconstructed image generated during video encoding of the current original frame image in the video to be encoded;
  • the distorted image may be obtained by filtering the reconstructed image by using other preset filtering methods, and the reconstructed image is generated during video encoding of the current original frame image in the encoded video.
  • the side information component may represent at least one of the following distortion features:
  • the first generation module 133 may be specifically configured to determine a distortion degree value of each pixel point of the distorted image; and use the acquired distortion degree value of each pixel point based on the position of each pixel point of the distorted image. Generating an edge information component corresponding to the distorted image; wherein each component value included in the side information component corresponds to a pixel point at the same position on the distorted image.
  • the first generating module 133 may be specifically configured to obtain a quantization parameter of each coding region of the current original frame image, and determine a quantization parameter of a coding region where each pixel of the distortion image is located as a distortion. The degree of distortion of each pixel of the image.
  • the first generating module 133 may be specifically configured to:
  • the obtained distortion degree value of each pixel point is normalized to obtain the processed distortion degree value, and the processed distortion degree value ranges from the pixel value range; the pixel based on the distorted image point
  • the position of the processed distortion level of each pixel is determined as the component value of the same position of the pixel in the side information component corresponding to the distorted image.
  • the embodiment of the present application further provides an electronic device, as shown in FIG. 14, including a processor 141, a communication interface 142, a memory 143, and a communication bus 144, wherein the processor 141 and the communication interface 142
  • the memory 143 completes communication with each other through the communication bus 144;
  • a memory 143 configured to store a computer program
  • the processor 141 is configured to implement a video encoding method step when executing a program stored on the memory.
  • the video encoding method includes:
  • the preset decision strategy determine whether to enable the preset de-distortion filtering function
  • the distorted image is generated during video encoding of the current original frame image in the video to be encoded;
  • the side information component represents a distortion feature of the distortion image relative to the current original frame image
  • the distortion image color component and the side information component of the distorted image are input into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component; wherein the CNN model is trained based on the preset training set, and the preset training is performed.
  • the set includes a original sample image, a distorted image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image;
  • Entropy coding the entropy coded data to obtain a coded video bitstream the entropy coded data is generated by video encoding the current original frame image, and includes decision information indicating whether to enable the preset demodulation filtering function, and for Generate side information of the side information component.
  • the embodiment of the present application further provides a computer readable storage medium.
  • the computer readable storage medium stores a computer program, and when the computer program is executed by the processor, the video encoding method step is implemented.
  • the video encoding method includes:
  • the preset decision strategy determine whether to enable the preset de-distortion filtering function
  • the distorted image is generated during video encoding of the current original frame image in the video to be encoded;
  • the side information component represents a distortion feature of the distortion image relative to the current original frame image
  • the distortion image color component and the side information component of the distorted image are input into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component; wherein the CNN model is trained based on the preset training set, and the preset training is performed.
  • the set includes a original sample image, a distorted image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image;
  • Entropy coding the entropy coded data to obtain a coded video bitstream the entropy coded data is generated by video encoding the current original frame image, and includes decision information indicating whether to enable the preset demodulation filtering function, and for Generate side information of the side information component.
  • the distortion image is de-distorted using a CNN model different from the related technology.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a compromise between coding performance and algorithm complexity.
  • the embodiment of the present application further provides a computer program, where the computer program is implemented by the processor to implement the video coding method step.
  • the video encoding method includes:
  • the preset decision strategy determine whether to enable the preset de-distortion filtering function
  • the distorted image is generated during video encoding of the current original frame image in the video to be encoded;
  • the side information component represents a distortion feature of the distortion image relative to the current original frame image
  • the distortion image color component and the side information component of the distorted image are input into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component; wherein the CNN model is trained based on the preset training set, and the preset training is performed.
  • the set includes an original sample image, and a distorted image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image;
  • Entropy coding the entropy coded data to obtain a coded video bitstream the entropy coded data is generated by video encoding the current original frame image, and includes decision information indicating whether to enable the preset demodulation filtering function, and for Generate side information of the side information component.
  • the distortion image is de-distorted using a CNN model different from the related technology.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a compromise between coding performance and algorithm complexity.
  • another embodiment of the present application further provides a video decoding apparatus, as shown in FIG.
  • the entropy decoding module 151 is configured to perform entropy decoding on the received video bitstream to obtain current entropy decoded data.
  • the second open determination module 152 is configured to determine whether to enable the preset de-distortion filtering function according to the decision information indicating whether to enable the preset de-distortion filtering function in the current entropy decoded data.
  • a second image obtaining module 153 configured to obtain a distorted image when the determining is turned on; the distorted image is generated in a process of performing video decoding on the current entropy decoded data;
  • a second generation module 154 configured to generate an edge information component corresponding to the distortion image based on the side information included in the current entropy decoding data; and the edge information component represents a distortion feature of the original frame image corresponding to the current entropy decoded data of the distortion image;
  • the second filter processing module 155 is configured to input the distortion image color component and the side information component of the distorted image into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component; wherein the CNN model is based on preset training
  • the preset training set includes the original sample image, and the distorted image color component of the plurality of distorted images corresponding to the original sample image, and the side information component corresponding to each distorted image.
  • the second open determination module 152 can be specifically configured to:
  • the current entropy decoded data is video-decoded to obtain a reconstructed image, and the reconstructed image is subjected to texture analysis; when the analysis result indicates that the texture is flat, it is determined that the preset de-distortion filtering function is turned on; when the analysis result indicates that the texture is complex, it is determined to turn off the preset de-distortion filtering. Function; or
  • the decision information directly indicating whether to enable the preset de-distortion filtering function is obtained from the current entropy decoded data, and based on the obtained decision information, determining whether to enable the preset de-distortion filtering function.
  • the distorted image may be a reconstructed image generated during video decoding of the current entropy decoded data
  • the distorted image may be obtained by filtering the reconstructed image by using other preset filtering methods, and the reconstructed image is generated during the process of video decoding the current entropy decoded data.
  • the resulting de-distorted image color component may be used to video decode subsequent entropy decoded data in the video bitstream;
  • It can be used to generate a decoded image corresponding to the original frame image as the to-be-played frame image.
  • the side information component may represent at least one of the following distortion features:
  • the second generating module 154 may be specifically configured to obtain, from the current entropy decoded data, a distortion degree value of each pixel point of the distorted image; and use the acquired position based on the position of each pixel point of the distorted image.
  • the distortion degree value of each pixel point generates an edge information component corresponding to the distorted image, wherein each component value included in the side information component corresponds to a pixel point at the same position on the distorted image.
  • the second generating module 154 may be specifically configured to: obtain, from the current entropy decoded data, a quantization parameter of each coding region of the original frame image corresponding to the current entropy decoded data, and each of the distortion images
  • the quantization parameter of the coding region where the pixel is located is determined as the distortion degree value of each pixel of the distortion image.
  • the second generating module 154 may be specifically configured to:
  • the obtained distortion degree value of each pixel point is normalized to obtain the processed distortion degree value, and the processed distortion degree value ranges from the pixel value range; the pixel based on the distorted image point
  • the position of the processed distortion level of each pixel is determined as the component value of the same position of the pixel in the side information component corresponding to the distorted image.
  • the distortion image is de-distorted using a CNN model different from the related art.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a compromise between decoding performance and algorithm complexity.
  • the embodiment of the present application further provides an electronic device, as shown in FIG. 16, including a processor 161, a communication interface 162, a memory 163, and a communication bus 164, wherein the processor 161 and the communication interface 162
  • the memory 163 completes communication with each other through the communication bus 164;
  • the processor 161 is configured to implement a video decoding method step when executing a program stored on the memory.
  • the video decoding method includes:
  • the distorted image is generated during video decoding of the current entropy decoded data
  • the edge information component represents a distortion feature of the original frame image corresponding to the current entropy decoded data of the distortion image
  • the distortion image color component and the side information component of the distorted image are input into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component; wherein the CNN model is trained based on the preset training set, and the preset training is performed.
  • the set includes an original sample image, and a distorted image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image.
  • the distortion image is de-distorted using a CNN model different from the related art.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a compromise between decoding performance and algorithm complexity.
  • the embodiment of the present application further provides a computer readable storage medium.
  • the computer readable storage medium stores a computer program.
  • the video decoding method step is implemented.
  • the video decoding method includes:
  • the distorted image is generated during video decoding of the current entropy decoded data
  • the edge information component represents a distortion feature of the original frame image corresponding to the current entropy decoded data of the distortion image
  • the distortion image color component and the side information component of the distorted image are input into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component; wherein the CNN model is trained based on the preset training set, and the preset training is performed.
  • the set includes an original sample image, and a distorted image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image.
  • the distortion image is de-distorted using a CNN model different from the related art.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a compromise between decoding performance and algorithm complexity.
  • the embodiment of the present application further provides a computer program, where the computer program is implemented by the processor to implement the video encoding method step.
  • the video decoding method includes:
  • the distorted image is generated during video decoding of the current entropy decoded data
  • the edge information component represents a distortion feature of the original frame image corresponding to the current entropy decoded data of the distortion image
  • the distortion image color component and the side information component of the distorted image are input into a pre-established CNN model for convolution filtering processing to obtain a de-distorted image color component; wherein the CNN model is trained based on the preset training set, and the preset training is performed.
  • the set includes an original sample image, and a distorted image color component of the plurality of distorted images corresponding to the original sample image, and an edge information component corresponding to each distorted image.
  • the distortion image is de-distorted using a CNN model different from the related technology.
  • the pre-established CNN model is used for de-distortion filtering, thereby achieving a compromise between decoding performance and algorithm complexity.
  • the communication bus mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus.
  • PCI Peripheral Component Interconnect
  • EISA Extended Industry Standard Architecture
  • the communication bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, only one thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.
  • the communication interface is used for communication between the above electronic device and other devices.
  • the memory may include a random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.
  • RAM random access memory
  • NVM non-volatile memory
  • the memory may also be at least one storage device located away from the aforementioned processor.
  • the processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; or may be a digital signal processing (DSP), an application specific integrated circuit ( Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component.
  • CPU central processing unit
  • NP network processor
  • DSP digital signal processing
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array

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Abstract

Selon un mode de réalisation, l'invention concerne des procédés et des dispositifs de codage et de décodage vidéo, et un appareil électronique. Le procédé consiste à : déterminer, selon un schéma de prise de décision prédéfini, s'il faut activer une fonction de filtrage de correction de distorsion prédéfinie; si la fonction de filtrage avec correction de distorsion est déterminée comme étant à activer, acquérir une image distordue; générer une composante d'information annexe correspondant à l'image distordue; entrer, dans un modèle CNN préétabli, une composante de couleur de l'image distordue et la composante d'information annexe pour effectuer un traitement de filtrage convolutionnel pour obtenir une composante de couleur d'une image à distorsion corrigée; et à effectuer un codage entropique sur des données à coder par entropie pour obtenir un flux de bits vidéo codé, les données à coder par entropie étant générées par réalisation d'un codage vidéo sur une image d'origine courante et comprenant des informations de prise de décision et des informations annexes pour la génération de la composante d'information annexe, et les informations de prise de décision indiquant si la fonction de filtrage avec correction de distorsion prédéfinie est à activer ou non. La solution met en oeuvre l'utilisation d'un modèle CNN pour effectuer un traitement de correction de distorsion sur une image distordue dans un processus de codage vidéo, et atteint un équilibre entre performance de codage et complexité de l'algorithme.
PCT/CN2018/083229 2017-06-27 2018-04-16 Procédés et dispositifs de codage et de décodage vidéo, et appareil électronique Ceased WO2019001094A1 (fr)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110211064A (zh) * 2019-05-21 2019-09-06 华南理工大学 一种基于边缘引导的混合退化文本图像恢复方法
CN110312134A (zh) * 2019-08-06 2019-10-08 杭州微帧信息科技有限公司 一种基于图像处理和机器学习的屏幕视频编码方法
CN111711815A (zh) * 2020-06-30 2020-09-25 福州大学 基于集成学习和概率模型的快速vvc帧内预测方法
CN111988621A (zh) * 2020-06-19 2020-11-24 新加坡依图有限责任公司(私有) 视频处理器训练方法、装置、视频处理装置及视频处理方法

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110062226B (zh) * 2018-01-18 2021-06-11 杭州海康威视数字技术股份有限公司 一种视频编码方法、视频解码方法、装置、系统及介质
CN111641831B (zh) * 2019-03-01 2023-09-01 杭州海康威视数字技术股份有限公司 帧内预测方法、装置、设备及存储介质
JP2022526107A (ja) * 2019-03-24 2022-05-23 オッポ広東移動通信有限公司 フィルタリング方法、装置、エンコーダ及びコンピュータ記憶媒体
CN111064958B (zh) * 2019-12-28 2021-03-30 复旦大学 一种针对b帧和p帧的低复杂度神经网络滤波算法
CN111405283B (zh) * 2020-02-20 2022-09-02 北京大学 基于深度学习的端到端视频压缩方法、系统及存储介质
CN114979711B (zh) * 2021-02-25 2023-10-13 华为技术有限公司 音视频或图像分层压缩方法和装置
CN114004743B (zh) * 2021-09-30 2025-05-09 浙江大华技术股份有限公司 图像重建、编码解码方法、重建模型训练方法、相关装置
WO2023133889A1 (fr) * 2022-01-17 2023-07-20 深圳市大疆创新科技有限公司 Procédé et appareil de traitement d'image, dispositif de commande à distance, système et support de stockage
CN114692742A (zh) * 2022-03-22 2022-07-01 中山大学 基于帧内块复制的自适应图像预处理方法、装置及介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102972022A (zh) * 2010-04-12 2013-03-13 松下电器产业株式会社 滤波器的定位和选择
CN105611303A (zh) * 2016-03-07 2016-05-25 京东方科技集团股份有限公司 图像压缩系统、解压缩系统、训练方法和装置、显示装置
CN205408063U (zh) * 2016-03-07 2016-07-27 京东方科技集团股份有限公司 图像压缩系统、解压缩系统、显示装置
WO2017066236A1 (fr) * 2015-10-15 2017-04-20 Cisco Technology, Inc. Filtre à boucle efficace pour un codec vidéo

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016127271A1 (fr) * 2015-02-13 2016-08-18 Xiaoou Tang Appareil et procédé pour réduire les artéfacts de compression d'une image compressée avec perte
KR102124714B1 (ko) * 2015-09-03 2020-06-19 미디어텍 인크. 비디오 코딩에서의 신경망 기반 프로세싱의 방법 및 장치

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102972022A (zh) * 2010-04-12 2013-03-13 松下电器产业株式会社 滤波器的定位和选择
WO2017066236A1 (fr) * 2015-10-15 2017-04-20 Cisco Technology, Inc. Filtre à boucle efficace pour un codec vidéo
CN105611303A (zh) * 2016-03-07 2016-05-25 京东方科技集团股份有限公司 图像压缩系统、解压缩系统、训练方法和装置、显示装置
CN205408063U (zh) * 2016-03-07 2016-07-27 京东方科技集团股份有限公司 图像压缩系统、解压缩系统、显示装置

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110211064A (zh) * 2019-05-21 2019-09-06 华南理工大学 一种基于边缘引导的混合退化文本图像恢复方法
CN110211064B (zh) * 2019-05-21 2022-11-18 华南理工大学 一种基于边缘引导的混合退化文本图像恢复方法
CN110312134A (zh) * 2019-08-06 2019-10-08 杭州微帧信息科技有限公司 一种基于图像处理和机器学习的屏幕视频编码方法
CN110312134B (zh) * 2019-08-06 2021-06-15 杭州微帧信息科技有限公司 一种基于图像处理和机器学习的屏幕视频编码方法
CN111988621A (zh) * 2020-06-19 2020-11-24 新加坡依图有限责任公司(私有) 视频处理器训练方法、装置、视频处理装置及视频处理方法
CN111711815A (zh) * 2020-06-30 2020-09-25 福州大学 基于集成学习和概率模型的快速vvc帧内预测方法
CN111711815B (zh) * 2020-06-30 2021-09-28 福州大学 基于集成学习和概率模型的快速vvc帧内预测方法

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