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WO2018187917A1 - Dispositif et procédé d'évaluation de qualité d'image - Google Patents

Dispositif et procédé d'évaluation de qualité d'image Download PDF

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Publication number
WO2018187917A1
WO2018187917A1 PCT/CN2017/079975 CN2017079975W WO2018187917A1 WO 2018187917 A1 WO2018187917 A1 WO 2018187917A1 CN 2017079975 W CN2017079975 W CN 2017079975W WO 2018187917 A1 WO2018187917 A1 WO 2018187917A1
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WO
WIPO (PCT)
Prior art keywords
picture
preprocessed
video file
edge
quality evaluation
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2017/079975
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English (en)
Chinese (zh)
Inventor
谢俊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Royole Technologies Co Ltd
Original Assignee
Shenzhen Royole Technologies Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Royole Technologies Co Ltd filed Critical Shenzhen Royole Technologies Co Ltd
Priority to PCT/CN2017/079975 priority Critical patent/WO2018187917A1/fr
Priority to CN201780004630.8A priority patent/CN108475430B/zh
Publication of WO2018187917A1 publication Critical patent/WO2018187917A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection

Definitions

  • the present invention relates to the field of image processing, and in particular, to a picture quality evaluation method and apparatus.
  • Video files in formats such as 360-degree video files and 3D video files do not have a uniform file identifier. Many players recognize whether the video file is in a 360-degree video format or a 3D video format by intercepting a picture frame in the video file. However, in the process of intercepting the picture frame, sometimes the picture with the solid color, the large area solid color or the low contrast is intercepted. This picture is not suitable as the basis for identifying the video type, and the picture frame needs to be re-interpreted after the recognition calculation. Recognizing again, resulting in a reduction in the efficiency of video format recognition.
  • the embodiment of the invention discloses a picture quality evaluation method and device, which improves the efficiency of video format recognition.
  • an embodiment of the present invention discloses a picture quality evaluation method, including:
  • the first picture is used to determine a target video file type when the N is greater than or equal to a first threshold.
  • an embodiment of the present invention discloses a picture quality evaluation apparatus, including:
  • a first acquiring module configured to acquire a first picture
  • a processing module configured to perform pre-processing on the first picture to obtain a pre-processed picture
  • a detecting module configured to perform edge detection on the preprocessed picture to count the number N of edge points,
  • the N is a positive integer
  • a determining module configured to use the first picture to determine a target video file type when the N is greater than or equal to the first threshold.
  • an embodiment of the present invention discloses a picture quality evaluation apparatus, including:
  • a processor coupled to the memory
  • the processor invokes the executable program code stored in the memory to perform all or part of the steps of the first aspect.
  • the first picture is obtained from the video file; secondly, the first picture is preprocessed to obtain a preprocessed picture; and again, the preprocessed picture is performed.
  • Edge detection to count the number N of edge points, the N is a positive integer; when the N is greater than or equal to the first threshold, the first picture is used to determine the target video file type.
  • the solution of the embodiment of the invention improves the efficiency of video format recognition.
  • FIG. 1 is a schematic flowchart of a picture quality evaluation method according to an embodiment of the present invention.
  • FIG. 2 is a schematic diagram of pixel points of a picture
  • FIG. 3 is a schematic diagram of another picture pixel point
  • FIG. 4 is a schematic flowchart diagram of another picture quality evaluation method according to an embodiment of the present disclosure.
  • FIG. 5 is a schematic structural diagram of a picture quality evaluation method according to an embodiment of the present invention.
  • FIG. 6 is a schematic structural diagram showing a partial structure of a picture quality evaluation method according to an embodiment of the present disclosure
  • FIG. 7 is a schematic partial structural diagram of a picture quality evaluation method according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of another picture quality evaluation method according to an embodiment of the present invention.
  • FIG. 1 is a schematic flowchart diagram of an image quality evaluation method according to an embodiment of the present invention. As shown in Figure 1, the method includes:
  • the picture quality evaluation apparatus acquires the first picture.
  • the first picture is a frame picture in the target video file.
  • the target video file is a video file of a type to be determined.
  • the target video file may be a VR video file, a 3D video file, a 360-degree video file, or other video files.
  • the first picture is a key frame in the target video file.
  • the key frame is the frame in which the key action in the motion or change of the character or object in the target video file is located.
  • the picture quality evaluation apparatus performs preprocessing on the first picture to obtain a preprocessed picture.
  • the pre-processing the first picture to obtain a pre-processed picture includes:
  • performing the resolution processing on the first picture by the picture quality evaluation apparatus is specifically: reducing the resolution of the first picture to a preset value, and using the reduced first picture as the second picture.
  • the preset value may be 640x480, 320x180, 567x390, 626x413 or other values.
  • the above preset value is 320x180.
  • the picture quality evaluation apparatus performs grayscale processing on the second picture, in particular, converting the second picture from a color picture to a gray picture, and using the gray picture as the third picture.
  • the above image quality evaluation apparatus performs histogram equalization processing on the third picture to make The third picture described above changes faster, avoiding that the third picture is a gradation picture and no edge is detected.
  • the picture quality evaluation apparatus performs edge detection on the preprocessed picture to count the number N of edge points, where N is a positive integer.
  • the performing edge detection on the preprocessed picture to count the number N of edge points includes:
  • Edge detection is performed on M pixel points in the preset area of the preprocessed picture to obtain M detection values, and the M detection values are in one-to-one correspondence with the M pixel points;
  • a pixel point corresponding to the second threshold value of the M detection values is used as an edge point to count the number N of edge points, and the M is greater than or equal to N.
  • FIG. 2 is a schematic diagram of pixel points of a preprocessed picture.
  • the black point is the pixel of the preprocessed picture.
  • FIG. 2 there are n columns of pixels on the horizontal axis and m rows of pixels on the vertical axis, and the size of the preprocessed picture may be mxn.
  • the preset region from the m-th row may be between 1 to 2 m-th row region in FIG. 2, FIG. 2 may also be in the region from the first column to the n 1 n 2 between the second column, may also FIG. 2 in between the m-th row to the m-th row 1 and column 2 intersection region between n 1 to n 2 of the second column.
  • the above m 1 is greater than or equal to 0 and less than or equal to m-2, and the above m 2 is greater than or equal to 1 and less than or equal to m-1.
  • the above n 1 is greater than or equal to 0 and less than or equal to n-2, and the above n 2 is greater than or equal to 1 and less than or equal to n-1.
  • the magnitudes of the above m 1 , m 2 , n 1 , and n 2 may be set according to the size of the preprocessed picture.
  • the preset area is the area between the 45th line and the 274th line in FIG. 3, that is, the 45th line and the last number in the picture shown in FIG. The area between 45 lines.
  • the picture quality evaluation apparatus After determining the preset area, the picture quality evaluation apparatus obtains the detection values one by one for the M pixel points in the preset area to obtain M detection values. The image quality evaluation apparatus then compares the M detection values one by one with the second threshold. If the detected value A is greater than the second threshold, the pixel corresponding to the detected value A is taken as an edge point. The detected value A is any one of the M detection values. Finally, the picture quality evaluation device counts the number of edge points to obtain the number N of edge points.
  • performing edge detection on M pixel points in a preset area of the preprocessed picture Testing including:
  • Edge detection is performed on M pixel points in a preset area of the preprocessed picture by a detection operator.
  • the above image quality evaluation device may also pass a Prewitt operator, a Canny operator, a Roberts operator, a Sobel operator, and Krisch.
  • An operator or other operator performs edge detection on M pixels in the preset area of the preprocessed picture.
  • the picture quality evaluation apparatus uses the first picture to determine a target video file type.
  • the method when the N is less than the first threshold, the method includes:
  • the picture quality evaluation apparatus may use the first picture corresponding to the pre-processed picture to confirm the type of the target video file. If it is determined that the above N is smaller than the first threshold, the picture quality evaluation apparatus acquires the fourth picture, and performs the processes described in the above steps S102-S104 again.
  • the fourth picture is a frame picture different from the first picture in the target video file, and the fourth picture is a key frame in the target video file.
  • the value of the first threshold is determined according to the size of the preprocessed picture.
  • the first threshold may be 1280 or other values.
  • the first picture is obtained from the video file; secondly, the first picture is preprocessed to obtain a preprocessed picture; and again, the preprocessed picture is performed.
  • Edge detection to count the number N of edge points, the N is a positive integer; when the N is greater than or equal to the first threshold, the first picture is used to determine the target video file type.
  • the solution of the embodiment of the invention improves the efficiency of video format recognition.
  • FIG. 4 is a flowchart showing another method for evaluating picture quality according to an embodiment of the present invention. intention. As shown in FIG. 4, the method includes:
  • the picture quality evaluation apparatus acquires the first picture.
  • the picture quality evaluation apparatus performs resolution processing on the first picture to obtain a second picture.
  • the second picture of the picture quality evaluation apparatus performs grayscale processing to obtain a third picture.
  • the third picture of the picture quality evaluation apparatus performs histogram equalization processing to obtain a preprocessed picture.
  • the picture quality evaluation apparatus performs edge detection on the M pixel points in the preset area of the preprocessed picture, and acquires M detection values.
  • the picture quality evaluation apparatus determines whether the M pixel points are edge points one by one, and counts the number N of edge points.
  • the picture quality evaluation apparatus determines whether the N is greater than or equal to a first threshold.
  • the picture quality evaluation apparatus performs step S408; otherwise, the picture quality evaluation apparatus performs step S409.
  • the picture quality evaluation apparatus uses the first picture to determine a type of the target video file.
  • the picture quality evaluation apparatus acquires a fourth picture, and performs the above steps S402-S407 again.
  • FIG. 5 is a schematic structural diagram of a picture quality evaluation apparatus according to an embodiment of the present invention. As shown in FIG. 5, the apparatus 500 includes:
  • the first obtaining module 501 is configured to acquire a first picture.
  • the first picture is a frame of pictures in the target video file.
  • the first picture is a key frame in the target video file.
  • the processing module 502 is configured to perform pre-processing on the first picture to obtain a pre-processed picture.
  • processing module 502 includes:
  • the first processing unit 5021 is configured to perform resolution processing on the target image to obtain a second image.
  • a second processing unit 5022 configured to perform grayscale processing on the second picture to obtain a third picture
  • the third processing unit 5023 is configured to perform a histogram equalization process on the third picture to obtain a pre-processed picture.
  • the detecting module 503 is configured to perform edge detection on the preprocessed picture to count the number N of edge points, where N is a positive integer.
  • the detecting module 503 further includes:
  • the detecting unit 5031 is configured to perform edge detection on the M pixel points in the preset area of the preprocessed picture to obtain M detection values, where the M detection values are in one-to-one correspondence with the M pixel points;
  • the statistic unit 5032 is configured to use a pixel point corresponding to the second threshold value of the M detection values as an edge point to count the number N of edge points, where the M is greater than or equal to N.
  • the detecting mode 303 is specifically configured to: perform edge detection on M pixel points in a preset area of the preprocessed picture by using a detection operator.
  • the determining module 504 is configured to use the first picture to determine a target video file type when the N is greater than or equal to the first threshold.
  • the apparatus 500 when the N is less than the first threshold, the apparatus 500 includes:
  • the second obtaining module 505 is configured to obtain a fourth picture, where the fourth picture is a frame picture different from the first picture in the target video file, and the picture quality evaluation operation is performed again based on the fourth picture.
  • each of the above modules (the first obtaining module 501, the processing module 502, the detecting module 503, the determining module 504, and the second acquiring module 505) is configured to execute the related steps of the method for determining the driving trajectory.
  • the “module” in this embodiment may be an application-specific integrated circuit (ASIC), a processor and memory that executes one or more software or firmware programs, integrated logic circuits, and/or others that may provide the above functions.
  • ASIC application-specific integrated circuit
  • the first acquiring module 501, The processing module 502, the detecting module 503, the determining module 504, and the second obtaining module 505 can be implemented by a processor in the apparatus 800 described in FIG.
  • the picture quality evaluation apparatus can be implemented by the structure in FIG.
  • the apparatus 800 includes at least one processor 801, at least one memory 802, and at least one communication interface 803.
  • the device may also include general components such as an antenna, which will not be described in detail herein.
  • the processor 801 can be a general purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the above program.
  • CPU central processing unit
  • ASIC application-specific integrated circuit
  • the communication interface 803 is configured to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Networks (WLAN), and the like.
  • RAN Radio Access Network
  • WLAN Wireless Local Area Networks
  • the memory 802 can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type that can store information and instructions.
  • the dynamic storage device can also be an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical disc storage, and a disc storage device. (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or can be used to carry or store desired program code in the form of instructions or data structures and can be Any other media accessed, but not limited to this.
  • the memory can exist independently and be connected to the processor via a bus.
  • the memory can also be integrated with the processor.
  • the memory 802 is configured to store application code that executes the above solution, and is controlled by the processor 801 for execution.
  • the processor 801 is configured to execute application code stored in the memory 802.
  • the picture quality evaluation apparatus shown in FIG. 8 the code stored in the memory 802 can perform the image quality evaluation method provided above, for example, the picture quality evaluation apparatus acquires the first picture; and preprocesses the first picture to obtain a preprocessed picture; Edge detection is performed on the preprocessed picture to count the number N of edge points, where N is a positive integer; when the N is greater than or equal to the first threshold, the first picture is used to determine a target video file. Types of.
  • the embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium may store a program, and the program includes some or all of the steps of any one of the picture quality evaluation methods described in the foregoing method embodiments.
  • the disclosed apparatus may be implemented in other ways.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or may be Integrate into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be electrical or otherwise.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present invention may contribute to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a memory. A number of instructions are included to cause a computer device (which may be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of the various embodiments of the present invention.
  • the foregoing memory includes: a U disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and the like, which can store program codes.
  • ROM Read-Only Memory
  • RAM Random Access Memory

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)

Abstract

La présente invention concerne un dispositif et un procédé d'évaluation de la qualité d'une image. Le procédé consiste : à acquérir une première image (S101) ; à réaliser un prétraitement sur la première image de façon à obtenir une image prétraitée (S102) ; à réaliser une détection de bord sur l'image prétraitée de façon à compter le nombre N de points de bord, N étant un nombre entier positif (S103) ; et lorsque N est supérieur ou égal à un premier seuil, à utiliser la première image pour déterminer le type d'un fichier vidéo cible (S104). Le procédé améliore l'efficacité de reconnaissance d'un format vidéo.
PCT/CN2017/079975 2017-04-10 2017-04-10 Dispositif et procédé d'évaluation de qualité d'image Ceased WO2018187917A1 (fr)

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Application Number Priority Date Filing Date Title
PCT/CN2017/079975 WO2018187917A1 (fr) 2017-04-10 2017-04-10 Dispositif et procédé d'évaluation de qualité d'image
CN201780004630.8A CN108475430B (zh) 2017-04-10 2017-04-10 图片质量评估方法及装置

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PCT/CN2017/079975 WO2018187917A1 (fr) 2017-04-10 2017-04-10 Dispositif et procédé d'évaluation de qualité d'image

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