CN111031301A - Method for adjusting color gamut space, storage device and display terminal - Google Patents
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Abstract
The invention provides a method for adjusting color gamut space, which comprises the following steps: s1, capturing at least one frame of picture; s2, processing the picture based on global color gamut mapping to obtain a compressed global picture; and S3, performing local color gamut mapping of a set region based on the obtained compression type global picture to obtain a video image subjected to dynamic mapping processing. The invention also discloses a storage device and a display terminal. According to the technical scheme, a new ICH color space is adopted on the basis of an LCH color space, and the color gamut spaces of BT.709 and BT.2020 are considered; the scheme adopts global and local color gamut mapping, and can improve the contrast of the whole image and reduce the details of the dark part.
Description
Technical Field
The present invention relates to the field of digital video processing, and in particular, to a method for adjusting a color gamut space, a storage device, and a display terminal.
Background
The high dynamic range image is coded based on a scene reference standard, real scene brightness information is stored by using more digits than a conventional image, and the high dynamic range image is widely applied to the fields of aerial remote sensing, virtual reality, medical images, cross-media publishing and the like. Because the dynamic range represented by the traditional display output equipment and the number of bits of processed images are limited, HDR images cannot be directly displayed and output, and the existing high-dynamic image display output equipment is expensive in manufacturing cost and difficult to popularize. Therefore, to display an output HDR image in a conventional low dynamic range device, the HDR image must be tone-compressed. How to make the compressed HDR image visually consistent with the subjective feeling of a natural scene is a direction of research in high dynamic range image processing technology.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for adjusting a color gamut space, a storage device, and a display terminal.
The technical scheme of the invention is realized as follows:
the invention discloses a method for adjusting color gamut space, which comprises the following steps:
s1, capturing at least one frame of picture;
s2, processing the picture based on global color gamut mapping to obtain a compressed global picture;
and S3, performing local color gamut mapping of a set region based on the obtained compression type global picture to obtain a video image subjected to dynamic mapping processing.
Further, the global color gamut mapping specifically includes: performing color gamut space conversion on the picture; performing color gamut compression on the converted picture; and performing color gamut space inverse conversion on the compressed picture.
Further, the local color gamut mapping specifically includes: selecting a regional block of a backlight and/or a dark part; and carrying out local mapping interpolation on the region block, and carrying out multi-scale fusion processing on the region block subjected to the local mapping interpolation processing.
Further, the local color gamut mapping specifically includes: selecting a regional block of a backlight and/or a dark part; and performing multi-scale fusion processing or histogram equalization processing on the section block.
The invention discloses a storage device, wherein a plurality of instructions are stored, the instructions are suitable for being loaded and executed by a processor, and the instructions are as follows:
s1, capturing at least one frame of picture;
s2, processing the picture based on global color gamut mapping to obtain a compressed global picture;
and S3, performing local color gamut mapping of a set region based on the obtained compression type global picture to obtain a video image subjected to dynamic mapping processing.
Further, the global color gamut mapping specifically includes: performing color gamut space conversion on the picture; performing color gamut compression on the converted picture; and performing color gamut space inverse conversion on the compressed picture.
Further, the local color gamut mapping specifically includes: selecting a regional block of a backlight and/or a dark part; carrying out local mapping interpolation on the region block; and performing multi-scale fusion processing on the local block subjected to the local mapping interpolation processing.
Further, the local color gamut mapping specifically includes: selecting a regional block of a backlight and/or a dark part; and performing multi-scale fusion processing or histogram equalization processing on the section block.
The invention discloses a display terminal, comprising: the processor is suitable for realizing all instructions, and the storage device is characterized by being the storage device.
The implementation of the method for adjusting the color gamut space, the storage device and the display terminal has the following beneficial technical effects:
the method is different from the defects of detail loss at the highlight position, weakened color saturation, reduced contrast of the dark part, HALO HALO caused by different sizes of local windows and large value of an intermediate brightness region in the local mapping technology due to improper color space conversion in the existing global mapping technology, and in the technical scheme, a new ICH color space is adopted on the basis of an LCH color space, and the color gamut spaces of BT.709 and BT.2020 are considered; the scheme adopts global and local color gamut mapping, and can improve the contrast of the whole image and reduce the details of the dark part.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for adjusting color gamut space according to an embodiment of the present invention;
FIG. 2 is a general framework of the method of the present invention;
FIG. 3 is a schematic diagram of the gamut space conversion process of FIG. 2;
FIG. 4 is a schematic illustration of the gamut sequential compression process of FIG. 2;
FIG. 5 is a schematic diagram of the interpolation process of FIG. 2;
FIG. 6 is a block diagram of a memory device according to an embodiment of the present invention;
fig. 7 is a schematic block diagram of a display terminal according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, in an embodiment, a method for adjusting a color gamut space includes:
s1, capturing at least one frame of picture;
s2, processing the picture based on global color gamut mapping to obtain a compressed global picture;
as shown in fig. 2, the global gamut mapping specifically includes: performing color gamut space conversion on the picture; performing color gamut compression on the converted picture; and performing color gamut space inverse conversion on the compressed picture.
Domain compression needs to be performed in a specific ICH space, so the global mapping is performed with gamut space conversion first, i.e. RGB- > ICH; and after the color gamut compression is finished, performing reverse conversion, namely ICH- > RGB, so that the front and rear color gamut spaces are kept as RGB.
The ICH color space is used, the conversion process simulates and describes a series of processes of human color vision transmission to the brain, compression and interconversion are facilitated, the ICH color space is used for compression, and color parameters of the ICH color space are independent of each other.
The sequential compression is adopted, wherein different brightness areas in the brightness compression are correspondingly compressed, the image effect is more conveniently directionally adjusted, in addition, the SDR area is divided into a core area and a compression area in the saturation compression, the highlight part is protected under the condition that most SDR content is well presented, the core area is kept unchanged, and the part outside the core area is compressed into the SDR area.
The global compression algorithm can be realized by the following two methods:
linear compression algorithm
The simplest processing mode in the global algorithm is to linearly compress the value of the high dynamic range to the low dynamic range, which does not consider visual factors nor image factors, and the converted image can keep the original contrast relationship, but the visual effect of the display image with the reduced dynamic range is inevitably lost. The bright and dark regions of the high dynamic range image are mutually interlaced and complicated, and the simple proportional compression cannot achieve the expected result. Generally, the mapping function selected by the global algorithm is non-linear, and may be pre-specified or derived from histogram statistics of the image, and is generally an exponential curve or other similar mapping curve.
Second, histogram adjustment algorithm
To date, the most excellent global algorithm is the histogram adjustment algorithm proposed by Larson et al in 1997, which is a clever improvement to histogram equalization. Larson considers that the human eye is sensitive to relative changes in image brightness and is insensitive to the brightness value itself, so that it is sufficient that bright areas are displayed brighter and dark areas are displayed darker in an image without the need to retain the exact absolute brightness intensity. The algorithm defines the change of the brightness level according to the human eye perception model, and the gray level in the image is redistributed by utilizing a mode of adjusting a histogram.
And S3, performing local color gamut mapping of a set region based on the obtained compression type global picture to obtain a video image subjected to dynamic mapping processing.
The local gamut mapping is specifically: selecting a regional block of a backlight and/or a dark part; and carrying out local mapping interpolation on the region block, and carrying out multi-scale fusion processing on the region block subjected to the local mapping interpolation processing.
Or: the localized gamut mapping specifically includes: selecting a regional block of a backlight and/or a dark part; and performing multi-scale fusion processing or histogram equalization processing on the section block.
In the process of image fusion, the weight proportion under the corresponding scale is calculated through a change curve according to the size of the image data with different scales, and finally the image data with different scales is fused with the original image data through the self-adaptive proportion to obtain the final output image data.
In the multi-scale fusion process, multiple (more than two) scales can be mixed with the original image, and in the generation of the adaptive proportional weight, the change curve can be replaced by a linear function, a piecewise function, a polynomial function and the like.
The image fusion adaptively generates corresponding scale proportion according to the features of different scales and then carries out final mixing, wherein a scale change curve L (x) can be flexibly changed, and mixed images with different effects can be generated.
The local compression algorithm has the following two types:
a camera-based compression algorithm:
in 2002, Reinhard et al proposed a photographic-based tone mapping algorithm. It firstly uses simple mapping function to map the brightness exceeding the output area range into the output block, and then carries on automatic exposure and shading (blanking) process to the area with strong local contrast, wherein the size of the area is related to the local contrast. dodging-and-burning is a photographic concept that refers to adding and subtracting brightness to and from dark and bright regions, respectively. Different area scale sizes may affect the scene reproduction in the extreme luminance area, and for high dynamic range images, any value may not guarantee the details of both the extremely bright and dark area scenes.
Compression algorithm of second, mean based on Retinex theory
The image is divided into a luminance part and a chrominance part by a compression algorithm of the Menylan based on Retinex theory, and the luminance part and the chrominance part are respectively processed. The processing of the luminance part, mylan, designs a filter that takes into account the high contrast area edge information in the image and performs the corresponding assignment operation by the distance of the pixel itself from the surrounding edge pixels, thus obtaining a new pixel value.
As described above, in global color gamut mapping, the essence of histogram equalization is to reduce the gray scale of an image in exchange for contrast expansion, so that the actual effective gray scale of the converted image is certainly less than or equal to the effective scale of the original image, high-frequency information of a partial image is lost, details of the image cannot be well maintained, and the processed image is poor in details;
the details of the low dynamic range image obtained by the local color gamut mapping camera compression algorithm are rich, but the algorithm stability is not high due to the fact that the subjective factor is provided when the region size is selected;
the algorithm of the Meylan avoids the occurrence of halos and the whitening phenomenon of low-contrast areas, ensuring good color reproduction. The method can keep important details of dark areas, increase local contrast of shadow areas and avoid halation, but highlight parts are excessively compressed by the method, and the display effect of bright areas of images is not good enough.
The invention designs a color gamut mapping scheme suitable for hardware implementation, on the premise of not generating the problems of flicker, color bands and the like, HDR videos can perfectly present the effect on SDR televisions, the overall contrast is strong, the details of dark parts are obvious, and hardware resources such as SRAM, bandwidth and the like are not excessively occupied.
The invention fully utilizes the independence of different parameters of the color gamut space to compress the color gamut from HDR to SDR on the basis of the existing color gamut standards of BT.709 and BT.2020, the compressed image can be divided into blocks and uniformly adjust the brightness and contrast of each region in different scales, and finally different regions are organically combined through the time correlation and the space correlation of the video image, thereby achieving the purposes of detail protection and contrast improvement.
The overall flow diagram of the present invention is shown in fig. 2, and the input includes two frames: i (n) is a current frame, namely a target frame, and H (n-1) is histogram equalization data of a previous frame; the output is histogram equalization data for the current frame, which will be output to the storage as input for the next frame H (n-1), f (n) is the final video image, which has undergone global mapping and local mapping, which can be directly displayed on the display device. As can be seen from the block diagram, i (n) first obtains data after gamut compression through global mapping, where the global mapping is divided into three parts, namely gamut space conversion, gamut compression and gamut space inverse conversion; and the compressed data is subjected to local mapping to obtain a video image subjected to local dynamic mapping processing, and the local mapping is divided into three parts, namely interpolation, histogram equalization and multi-scale fusion.
The respective portions are explained in detail below.
The color gamut space conversion sequence is shown in fig. 3, and the input video image will be compressed on the ICH space after a series of conversions. The human eye has photoreceptors (cones), each cone being particularly sensitive to long (L), medium (M) or short (S) wavelengths. The eye adapts to the light in a nonlinear signal response, and the light signal is compressed to reduce the dynamic range. The output of the nonlinear signal response can extract important information and separate the signal into three different components through a color difference process, and the conversion process simulates and describes a series of processes of human color vision transmission to the brain, wherein the key ICH conversion method is as follows:
I＝I (1)
equations (1), (2) and (3) are the conversion equations for ICH, where I is luminance and C is saturationDegree of neutralization, H is hue angle, CT、CPIs IC in standard REC.2100TCPThe parameters in (3) can be relatively independent parameters in each color gamut space according to a conversion formula.
In the color gamut conversion, the conversion method calculation of the ICH may be replaced with a form of an absolute value, a sine function, or the like as in the formula (2).
As shown in fig. 4, the gamut compression scheme firstly separates the regions into an HDR region and an SDR region, where the SDR region can be normally displayed on an SDR television, and the HDR region displayed directly on the SDR television may have the problems of too bright dark portion, loss of highlight details, and the like. Here, 90% of the SDR area is set as the core area, and content exceeding 90% of the SDR area is set as the compression area without change, and the compression is performed with a gradient. The color gamut compression adopts a sequential compression method, namely, luminance compression is firstly carried out and then protection degree compression is carried out. Firstly, compressing the brightness in the direction I, wherein the specific expression is as follows:
I＝weightsl*Il+weightsm*Im+weightsh*Ih
in the above formula, I of the ICH space is divided into low brightness, middle brightness and high brightness, and different compression weights can be selected for different brightness parts, wherein weightl、weightm、weighthRespectively representing low brightness, medium brightness and high brightness weight, effectively protecting high brightness details through gradient compression, controlling the change speed of the medium brightness part and enhancing the details of the dark part area.
And then compressing in the saturation C direction, compressing the content which exceeds 90% of the SDR area into the 90-100% area of the SDR, wherein the specific expression is as follows:
C＝CSDR_90+CHDR*CSDR_10/(CHDR-CSDR_90)
CSDR_90meaning 90% boundary value, C, of the SDR regionHDRDenotes the HDR boundary value, CSDR_10Refers to a 90% increase over the SDR area.
The left graph shows the distribution of the image based on brightness and saturation, and the distribution range that can be represented by SDR and the distribution range that can be represented by HDR are respectively drawn.
The middle graph is based on the I direction, i.e. the luma direction, for gamut compression, so that most of the HDR image beyond the SDR portion is compressed a part into the SDR visible region, which has the advantage of compressing more highlights into the SDR to see more highlight details based on luma segmentation compression, while the lower luma is not over-compressed, and the dark details are well preserved.
The right graph shows that the middle graph is compressed based on the brightness I, and then the saturation C direction is subjected to HDR high saturation compression to the internal region of the SDR domain, and the size in the SDR domain is unchanged, and the HDR domain is changed.
And (4) performing inverse transformation of the color gamut space, namely performing an inverse process of the color gamut space, and performing inverse solution.
In color gamut compression, the sequential compression may be saturation-first followed by luminance compression, or simultaneous compression, etc., and the core region may be 95%, 80%, 70%, etc., of the SDR region.
Local map interpolation
As shown in fig. 5, there are two local map interpolation inputs, one is the globally mapped video image I' (n), and the other is the histogram equalized intermediate data H (n-1) of the previous frame. After the video image I' (n) is partitioned according to different scales, each block obtains image data after balancing each block through histogram balancing intermediate data, and finally, local mapping image data f (n) of different scales are obtained through bilinear interpolation according to the space relation between the blocks. The specific expression is as follows:
f(n)＝Da*Ha+Db*Hb+Dc*Hc+Dd*Hd
wherein D is the distance between the local processing block and the adjacent different block, and H is the image data after the equalization processing of the adjacent block.
And a, b, c and d represent four blocks closest to the current local processing block, Da, Db, Dc and Dd are distances between the four blocks and the current local processing block respectively, and Ha, Hb, Hc and Hd are data of the four blocks after histogram equalization processing respectively.
After being input, a video image I' (n) is uniformly divided into equal-part rectangular blocks, and the equal-part rectangular blocks are divided into a large scale and a small scale according to the scale size, wherein the smaller the scale is, the stronger the contrast is. And then calculating the CDF cumulative probability value of each block by an orthogonal Legendre transform polynomial to obtain a corresponding transform intermediate value H (n).
The orthogonal Legendre transform polynomial may approximate a continuous irreducible curve by polynomial simulation, and the infinite continuous data of histogram equalization may be replaced by finite discrete data. Assuming that an image is M pixels long and N pixels wide, the whole image is divided into 32x32 blocks, and the bit width k bits luminance component is taken as an example, the operation MxNx2+2 is requiredkNext, the required SRAM memory space is 32x32x2kxK bits; the operation times are changed into M x N xs times after s (generally less than 9 and adjustable) order orthogonal Legendre transformation is adopted, and the required SRAM space is 32x32xsxK bits. Under the condition of adding little logic calculation, the SRAM space is greatly reduced, so that the area cost of hardware implementation is reduced. This transformed intermediate value H (n) is output to the storage as input H (n-1) of the next frame, and the histogram equalization data of each block can be obtained by interpolating the transformed intermediate values H (n) of the four blocks adjacent thereto.
The local mapping image data f (n) has different local mapping effects due to different block scales, the smaller the scale is, the more concentrated the brightness is, and the local contrast is strong but the overall contrast is weak; the larger the scale, the more uniform the brightness distribution, but the smaller the local contrast. In order to enhance local contrast, enhance dark part details and protect highlight details, multi-scale image data can be mixed in an adaptive proportion mode. The small scale proportion of the dark part area is increased, the large scale proportion of the highlight area is increased, and the specific expression is as follows:
L(x)＝a*x+b
weights＝L(f(n))
F(n)＝weightss*fs(n)+weights*f(n)+weightsB*fB(n)
weightss+weights+weightsB＝1
l (x) × x + b represents a mapping curve used for calculating weights, where a linear function is taken as an example, x is an input, l (x) is an output, and a and b are fixed coefficients, which can be adjusted according to experimental results.
In the above formula, l (x) is different mapping curves, that is, f (n) is used as input, and different curves (such as log functions) are used for calculation to obtain weights of the global result, the small-scale local mapping result, and the large-scale local mapping result, respectively. f. ofs(n) denotes the results of the small scale partial mapping, weightssIs its corresponding weight; f (n) is the image data after global mapping, and weights are the corresponding weights; f. ofB(n) denotes the result of the large scale partial mapping, weightsBFor which the corresponding weight is. The function calculates the weight proportion (weights) under the corresponding scale according to the size of the image data of different scales, and the image data of different scales is fused by the self-adaptive proportion to obtain the final output image data F (n).
The following describes an apparatus for implementing the above method, and reference may be made to the above method description for parts not described in the apparatus.
Referring to fig. 6, a memory device 10 is provided in which a plurality of instructions are stored, the instructions being adapted to be loaded and executed by a processor to:
s1, capturing at least one frame of picture;
s2, processing the picture based on global color gamut mapping to obtain a compressed global picture;
and S3, performing local color gamut mapping of a set region based on the obtained compression type global picture to obtain a video image subjected to dynamic mapping processing.
Global gamut mapping, specifically: performing color gamut space conversion on the picture; performing color gamut compression on the converted picture; and performing color gamut space inverse conversion on the compressed picture.
The local gamut mapping is specifically: selecting a regional block of a backlight and/or a dark part; carrying out local mapping interpolation on the region block; and performing multi-scale fusion processing on the local block subjected to the local mapping interpolation processing.
Or, the local color gamut mapping specifically includes: selecting a regional block of a backlight and/or a dark part; and performing multi-scale fusion processing or histogram equalization processing on the section block.
Referring to fig. 7, a display terminal 100 includes: a processor 200 adapted to implement instructions, and a storage device, such as the storage device 10 described above.
The display terminal 100 can be a display device in a series of image video processing devices or schemes such as a digital television, an OTT box, a vehicle-mounted central control device, a monitoring device and the like.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit or scope of the embodiments of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.
Claims (9)
1. A method of gamut space adjustment, comprising:
s1, capturing at least one frame of picture;
s2, processing the picture based on the global color gamut mapping to obtain a compressed global picture;
and S3, performing local color gamut mapping of the set region based on the obtained compressed global picture, and obtaining a video image subjected to dynamic mapping processing.
2. The method according to claim 1, wherein the global gamut mapping is specifically: performing color gamut space conversion on the picture; performing color gamut compression on the converted picture; and performing color gamut space inverse conversion on the compressed picture.
3. The method according to claim 1, wherein the localized gamut mapping is specifically: selecting a regional block of a backlight and/or a dark part; and carrying out local mapping interpolation on the region block, and carrying out multi-scale fusion processing on the region block subjected to the local mapping interpolation processing.
4. The method according to claim 1, wherein the localized gamut mapping specifically comprises: selecting a regional block of a backlight and/or a dark part; and performing multi-scale fusion processing or histogram equalization processing on the section block.
5. A memory device having stored therein a plurality of instructions, the instructions adapted to be loaded and executed by a processor to:
s1, capturing at least one frame of picture;
s2, processing the picture based on the global color gamut mapping to obtain a compressed global picture;
and S3, performing local color gamut mapping of the set region based on the obtained compressed global picture, and obtaining a video image subjected to dynamic mapping processing.
6. The storage device according to claim 5, wherein the global gamut mapping is specifically: performing color gamut space conversion on the picture; performing color gamut compression on the converted picture; and performing color gamut space inverse conversion on the compressed picture.
7. The storage device according to claim 5, wherein the localized gamut mapping is specifically: selecting a regional block of a backlight and/or a dark part; carrying out local mapping interpolation on the region block; and performing multi-scale fusion processing on the local block subjected to the local mapping interpolation processing.
8. The storage device according to claim 5, wherein the localized gamut mapping specifically comprises: selecting a regional block of a backlight and/or a dark part; and performing multi-scale fusion processing or histogram equalization processing on the section block.
9. A display terminal, comprising: a processor and a memory device, the processor being adapted to implement instructions in the memory device, wherein the memory device is a memory device according to any one of claims 5 to 8.
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