WO2020056767A1 - 视频图像分量的预测方法、装置及计算机存储介质 - Google Patents

视频图像分量的预测方法、装置及计算机存储介质 Download PDF

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Publication number
WO2020056767A1
WO2020056767A1 PCT/CN2018/107109 CN2018107109W WO2020056767A1 WO 2020056767 A1 WO2020056767 A1 WO 2020056767A1 CN 2018107109 W CN2018107109 W CN 2018107109W WO 2020056767 A1 WO2020056767 A1 WO 2020056767A1
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Prior art keywords
image component
value
reference value
pixel point
coding block
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English (en)
French (fr)
Inventor
霍俊彦
马彦卓
柴小燕
万帅
杨付正
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Priority to PCT/CN2018/107109 priority Critical patent/WO2020056767A1/zh
Priority to CN201880094931.9A priority patent/CN112313950B/zh
Publication of WO2020056767A1 publication Critical patent/WO2020056767A1/zh
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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
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/103—Selection of coding mode or of prediction mode
    • 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

Definitions

  • the embodiments of the present application relate to the technical field of video encoding and decoding, and in particular, to a method, a device, and a computer storage medium for predicting video image components.
  • H.265 / High Efficiency Video Coding is the latest international video compression standard.
  • the compression performance of H.265 / HEVC is better than the previous generation video coding standard H.264 / Advanced Video Coding. , AVC) increased by about 50%, but still can not meet the needs of rapid development of video applications, especially new high-definition, virtual reality (Virtual Reality, VR) and other new video applications.
  • JEM Joint Exploration Test Model
  • VVC Versatile Video Coding
  • the embodiments of the present application expect to provide a method, a device, and a computer storage medium for predicting video image components, which can effectively improve the prediction accuracy of the video image components and make the predicted values of the video image components closer to the video image components The original value, which saves the coding rate.
  • an embodiment of the present application provides a method for predicting a video image component, where the method includes:
  • first image component reconstruction value represents that at least one pixel of the coding block corresponds to A reconstruction value of a first image component, the first image component neighboring reference value and the second image component neighboring reference value respectively representing each neighboring pixel point in the coding block neighboring reference pixel corresponding to the first image
  • a second image component prediction value corresponding to each pixel in the coding block is obtained.
  • an embodiment of the present application provides a device for predicting a video image component.
  • the device for predicting a video image component includes: an acquiring part, a determining part, and a predicting part;
  • the acquiring section is configured to acquire a first image component reconstruction value, a first image component adjacent reference value, and a second image component adjacent reference value corresponding to a coding block; wherein the first image component reconstruction value represents the At least one pixel of the coding block corresponds to the reconstructed value of the first image component, and the first image component neighboring reference value and the second image component neighboring reference value respectively represent each phase in the coding block neighboring reference pixel.
  • the neighboring pixel points correspond to the reference value of the first image component and the reference value of the second image component;
  • the determining section is configured to determine a model parameter according to the acquired first image component reconstruction value, the first image component adjacent reference value, and the second image component adjacent reference value;
  • the prediction section is configured to obtain, according to the model parameter, a prediction value of a second image component corresponding to each pixel in the coding block.
  • an embodiment of the present application provides a device for predicting a video image component.
  • the device for predicting a video image component includes: a memory and a processor;
  • the memory is configured to store a computer program capable of running on the processor
  • the processor is configured to execute the steps of the method according to the first aspect when the computer program is run.
  • an embodiment of the present application provides a computer storage medium that stores a prediction program for a video image component, where the prediction program for a video image component is executed by at least one processor to implement the first aspect. The steps of the method described.
  • Embodiments of the present application provide a method, a device, and a computer storage medium for predicting a video image component, by acquiring a first image component reconstruction value corresponding to a coding block, a first image component adjacent reference value, and a second image component adjacent reference
  • the reconstruction value of the first image component represents the reconstruction value of at least one pixel of the coding block corresponding to the first image component, the first image component neighboring reference value and the second image component neighboring reference
  • the values respectively represent the reference value corresponding to the first image component and the reference value of the second image component of each adjacent pixel point in the adjacent reference pixels of the coding block; according to the reconstructed value of the first image component, the first An image component neighboring reference value and the second image component neighboring reference value are used to determine a model parameter; according to the model parameter, a second image component predicted value corresponding to each pixel in the coding block is obtained;
  • the determination of the model parameters in the embodiment of the application not only considers the first image component neighboring reference value and the
  • FIGS. 1A to 1C are schematic structural diagrams of video image sampling formats in related technical solutions
  • FIG. 2A and FIG. 2B are schematic diagrams of sampling a first image component neighboring reference value and a second image component neighboring reference value of a coding block in a related technical solution;
  • 3A to 3C are schematic structural diagrams of a CCLM preset model in a related technical solution
  • FIG. 4 is a grouping schematic diagram of the first image component neighboring reference value and the second image component neighboring reference value in the MMLM prediction mode in the related technical solution;
  • FIG. 5 is a schematic diagram of distribution of each pixel point and neighboring reference pixels in a coding block according to an embodiment of the present application
  • FIG. 6 is a schematic block diagram of a video encoding system according to an embodiment of the present application.
  • FIG. 7 is a schematic block diagram of a video decoding system according to an embodiment of the present application.
  • FIG. 8 is a schematic flowchart of a method for predicting a video image component according to an embodiment of the present application
  • FIG. 9 is a schematic structural diagram of a device for predicting a video image component according to an embodiment of the present application.
  • FIG. 10 is a schematic structural diagram of another apparatus for predicting a video image component according to an embodiment of the present application.
  • FIG. 11 is a schematic structural diagram of another apparatus for predicting a video image component according to an embodiment of the present application.
  • FIG. 12 is a schematic diagram of a specific hardware structure of a video image component prediction apparatus according to an embodiment of the present application.
  • a first image component, a second image component, and a third image component are generally used to characterize a coding block; wherein the three image components are a luminance component, a blue chrominance component, and a red chrominance component, respectively.
  • the luminance component is usually expressed by the symbol Y
  • the blue chrominance component is usually expressed by the symbol Cb
  • the red chrominance component is usually expressed by the symbol Cr.
  • the first image component may be a luminance component Y
  • the second image component may be a blue chrominance component Cb
  • the third image component may be a red chrominance component Cr
  • the currently commonly used sampling format is the YCbCr format.
  • the YCbCr format includes the following types, as shown in Figures 1A to 1C, where the cross (X) in the figure represents the sampling point of the first image component, and the circle ( ⁇ ) represents the second Image component or third image component sampling point.
  • the YCbCr format includes:
  • the video image uses the 4: 2: 0 format of YCbCr
  • the first image component of the video image is a 2N ⁇ 2N encoding block
  • the corresponding second image component or third image component is N ⁇ N size Coded block, where N is the side length of the coded block.
  • the following description will be described by taking the 4: 2: 0 format as an example, but the technical solutions of the embodiments of the present application are also applicable to other sampling formats.
  • CCLM Cross-component linear model prediction
  • H.266 CCLM implements the prediction from the first image component to the second image component, the first image component to the third image component, and the prediction between the second image component and the third image component.
  • the prediction from the component to the second image component is described as an example, but the technical solution in the embodiment of the present application can also be applied to the prediction of other image components.
  • the first image component and the second image component are of the same coding block, and the second image component is The prediction is performed based on the reconstructed value of the first image component of the same coding block, for example, using a preset model as shown in equation (1):
  • i, j represent the position coordinates of the sampling points in the coding block
  • i represents the horizontal direction
  • j represents the vertical direction
  • Pred C [i, j] represents the sampling points whose position coordinates in the coding block are [i, j].
  • the second image component prediction value, Rec Y [i, j] represents the reconstructed value of the first image component corresponding to the sampling point with the position coordinate [i, j] in the same coding block (downsampled)
  • ⁇ and ⁇ are the above
  • the model parameters of the preset model can be derived by minimizing the regression error of the first image component neighboring reference value and the second image component neighboring reference value around the coding block, as calculated using equation (2):
  • Y (n) represents the adjacent reference values of all the first image components on the left and upper sides after downsampling
  • C (n) represents the adjacent reference values of all the second image components on the left and upper sides
  • FIG. 2A and FIG. 2B which are schematic diagrams of sampling a first image component neighboring reference value and a second image component neighboring reference value of a coding block in a related technical solution, respectively.
  • the bold The larger box is used to highlight the first image component coding block 21
  • the gray solid circle is used to indicate the adjacent reference value Y (n) of the first image component coding block 21; in FIG.
  • FIG. 2A shows a 2N ⁇ 2N size first image component coding block 21, and for a 4: 2: 0 format video image, the size of a 2N ⁇ 2N size first image component corresponding to the size of the second image component Is N ⁇ N, as shown in 22 in FIG. 2B; that is, FIG. 2A and FIG. 2B are schematic diagrams of encoding blocks obtained by sampling the first image component and the second image component respectively for the same encoding block.
  • formula (2) can be directly applied; for non-square coding blocks, the neighboring samples with longer edges are first down-sampled to obtain the number of samples equal to the number of samples with shorter edges.
  • ⁇ and ⁇ do not need to be transmitted, and can also be calculated by using formula (2) in the decoder; in the embodiment of the present application, this is not specifically limited.
  • FIGS. 3A to 3C are schematic diagrams illustrating the principle structure of a CCLM preset model in related technical solutions.
  • a, b, and c are adjacent reference values of the first image component
  • A, B, and C Is the second image component neighboring reference value
  • e is the reconstructed value of the first image component corresponding to a pixel in the coding block
  • E is the predicted value of the second image component corresponding to the pixel
  • the image component adjacent reference value Y (n) and the second image component adjacent reference value C (n) can be calculated according to formula (2), ⁇ and ⁇ , and according to the calculated ⁇ and ⁇ , and formula (1), Establish a preset model, as shown in Figure 3C; bring the reconstruction value e of the first image component corresponding to a pixel in the coding block into the preset model described in equation (1), and calculate the first corresponding to the pixel Two image component prediction values E.
  • CCLM prediction modes there are currently two CCLM prediction modes: one is a single model CCLM prediction mode; the other is a multiple model CCLM (Multiple Model CCLM, MMLM) prediction mode, also known as a MMLM prediction mode.
  • the single model CCLM prediction mode has only one preset model to predict the second image component from the first image component; and the MMLM prediction mode has multiple preset models to achieve the prediction from the first image component.
  • the second image component For example, in the prediction mode of MMLM, the first image component neighboring reference value and the second image component neighboring reference value of the coding block are divided into two groups, and each group can be used separately as a training for deriving model parameters in a preset model.
  • each group can derive a set of model parameters ⁇ and ⁇ ; and the reconstructed value of the first image component of the coding block can also be grouped according to the classification method of the adjacent reference values of the first image component, and use the corresponding
  • the model parameters ⁇ and ⁇ are used to establish a preset model.
  • FIG. 4 shows a grouping schematic diagram of the first image component neighboring reference value and the second image component neighboring reference value in the MMLM prediction mode in the related technical solution; wherein the threshold value is used to indicate the basis for establishing multiple preset models
  • the set value and the threshold value are obtained by averaging the adjacent reference values Y (n) of the first image component.
  • the threshold value is represented by Threshold, and Threshold is used as the dividing point.
  • the component neighboring reference value is less than or equal to the threshold, it is divided into the first group; if the first image component neighboring reference value is greater than the threshold, it is divided into the second group; here, according to the first image component neighboring reference value of the first group
  • Rec Y [i, j] represents the reconstructed value of the first image component corresponding to the position coordinates in the coding block at [i, j] pixels;
  • Pred 1C [i, j] represents the position coordinates in the coding block at [i, j ] Pixel points are predicted values of the second image component according to the first preset model M1
  • Pred 2C [i, j] indicates that the position coordinates in the coding block are [i, j] pixels are obtained according to the second preset model M2 The predicted value of the second image component.
  • the first and second image component neighboring reference values of the coding block are used to calculate the model parameters ⁇ and ⁇ of the preset model. Specifically, ⁇ and ⁇ are minimized The regression error between the first image component neighboring reference value and the second image component neighboring reference value is obtained, as shown in the above formula (2).
  • the image spatial texture often changes, and the distribution characteristics of pixels in different regions are different. For example, some pixels are high brightness and some pixels are low brightness. If you simply use adjacent reference pixels to Because the model parameters for constructing the preset model are not comprehensive enough, the constructed model parameters are not optimal, so that the predicted value of the second image component obtained by the preset model is not accurate enough.
  • a prediction method of a video image component proposes to construct a model parameter of a preset model based on a first image component reconstruction value and a second image component temporary value of a coding block, wherein the second image component temporary value is Obtained according to the degree of similarity between the reconstructed value of the first image component of the coding block and the adjacent reference value of the first image component, so that the constructed model parameters are as close as possible to the optimal model parameters; see FIG. 5, which shows A schematic diagram of the distribution of each pixel point and adjacent reference pixels in a coding block provided in the embodiment of the present application; as shown in FIG.
  • the neighboring reference pixels of the coding block are mainly high-brightness, and the pixel points in the coding block are based on Medium to low brightness is dominant; because the embodiment of the present application not only considers the first image component adjacent reference value and the second image component adjacent reference value corresponding to adjacent reference pixels, but also considers the first image component reconstruction value and the first image component adjacent reference value.
  • the degree of similarity between adjacent reference values of an image component makes the constructed model parameters as close to the optimal model parameters as possible.
  • the reconstruction value of the first image component of the coding block not only participates in the application of the preset model, but also participates in the calculation of the model parameters, which further makes the predicted value of the second image component obtained by the embodiment of the present application even more. Close to the original value of the second image component.
  • the video encoding system 600 includes transformation and quantization 601, intra estimation 602, intra prediction 603, motion compensation 604, and motion.
  • a video coding block can be obtained by dividing the coding tree block (Coding Tree Unit, CTU), and then transform and quantize 601 Transform the video coding block, including transforming the residual information from the pixel domain to the transform domain, and quantizing the resulting transform coefficients to further reduce the bit rate;
  • intra estimation 602 and intra prediction 603 are used to The video encoding block performs intra prediction; specifically
  • the residual block is reconstructed in the pixel domain.
  • the reconstructed residual block is filtered by the filter control analysis 607 and deblocking filtering and SAO filtering 608 to remove the block effect Artifacts, and then add this reconstructed residual block to a predictive block in the frame of the decoded image buffer 610 to produce a reconstructed Video encoding block; header information encoding and CABAC 609 are used to encode the quantized transform coefficients.
  • the context content can be based on neighboring encoding blocks and can be used to encode information indicating the determined intra prediction mode.
  • the code stream of the video signal is output; and the decoded image buffer 610 is used to store reconstructed video encoding blocks. As the video image encoding progresses, new reconstructed video encoding blocks are continuously generated. These reconstructed videos The encoded blocks are stored in the decoded image buffer 610.
  • the video decoding system 700 includes header information decoding and CABAC decoding 701, inverse transform and inverse quantization 702, intra prediction 703, Motion compensation 704, deblocking filtering and SAO filtering 705 and decoded image buffer 706 and other components; after the input video signal is subjected to the encoding processing in FIG.
  • the code stream of the video signal is output; the code stream is input into the video decoding system 700, first After header information decoding and CABAC decoding 701, it is used to obtain the decoded transform coefficients; the transform coefficients are processed by inverse transform and inverse quantization 702 to generate residual blocks in the pixel domain; intra prediction 703 can be used to The determined intra-prediction mode and data from a previously decoded block of the current frame or picture generate prediction data for the current video decoded block; motion compensation 704 is determined by parsing the motion vector and other associated syntax elements to determine the video decoded block Prediction information, and use the prediction information to generate predictive blocks of the video decoding block being decoded; The residual block of 702 is summed with the corresponding predictive block generated by motion compensation 704 to form a decoded video block; the decoded video signal is deblocked and SAO filtered 705 in order to remove block effect artifacts, which can be improved Video quality; the decoded video block is then stored in the decoded image buffer 706,
  • the embodiment of the present application is mainly applied to the intra prediction part 603 as shown in FIG. 6 and the intra prediction part 703 as shown in FIG. 7; that is, the embodiment of the present application can work simultaneously on the encoding system and the decoding system.
  • the application example does not specifically limit this.
  • FIG. 8 it illustrates a method for predicting a video image component according to an embodiment of the present application.
  • the method may include:
  • S801 Obtain a first image component reconstruction value, a first image component adjacent reference value, and a second image component adjacent reference value corresponding to a coding block; wherein the first image component reconstruction value represents at least one pixel of the coding block Point corresponds to the reconstruction value of the first image component, and the first image component neighboring reference value and the second image component neighboring reference value respectively represent each neighboring pixel point in the coding block neighboring reference pixel corresponding to the first A reference value of an image component and a reference value of a second image component;
  • S802 Determine a model parameter according to the acquired reconstruction value of the first image component, the first image component neighboring reference value, and the second image component neighboring reference value;
  • S803 Obtain a second image component prediction value corresponding to each pixel in the coding block according to the model parameters.
  • the coding block is the current coding block for which the second image component prediction or the third image component prediction is to be performed; the first image component reconstruction value is used to characterize the first image component corresponding to at least one pixel in the coding block.
  • Reconstruction value, the first image component neighboring reference value is used to characterize the reference value of the first image component corresponding to the neighboring reference pixel point of the coding block, and the second image component neighboring reference value is used to represent the neighboring reference pixel point of the coding block The reference value of the corresponding second image component.
  • the first image component reconstruction value, the first image component adjacent reference value, and the second image component adjacent reference value corresponding to the coding block are obtained; wherein the first image component reconstruction Value represents the reconstruction value of at least one pixel point of the coding block corresponding to the first image component, and the first image component neighboring reference value and the second image component neighboring reference value respectively represent the coding block neighboring reference pixel
  • the first image component reconstruction Value represents the reconstruction value of at least one pixel point of the coding block corresponding to the first image component
  • the first image component neighboring reference value and the second image component neighboring reference value respectively represent the coding block neighboring reference pixel
  • the optimal model parameters of the preset model are analyzed from the perspective of mathematical theory.
  • the original value of the second image component and the second image component are generally expected.
  • i and j represent the position coordinates of pixels in the coding block
  • i represents a horizontal direction
  • j represents a vertical direction
  • C [i, j] is the second image corresponding to the pixel with the position coordinate [i, j] in the encoding block Component original value
  • C Pred [i, j] is the predicted value of the second image component corresponding to the pixel point whose position coordinate is [i, j] in the coding block
  • the optimal model parameters ⁇ opt and ⁇ opt of the preset model It can be obtained by the least square method, as shown in equation (5),
  • the second image component temporary value is constructed based on the similarity between the first image component reconstruction value of the coding block and the first image component neighboring reference value, and the constructed second image component
  • the temporary value replaces the original value of the second image component corresponding to each pixel in the coding block; in this case, the optimal model parameters can be obtained without introducing additional bit overhead.
  • the reconstructed value according to the acquired first image component, the neighboring image reference value of the first image component, and the second image component phase are obtained.
  • Neighbor reference values to determine model parameters including:
  • a model parameter is determined according to the first image component reconstruction value and the acquired second image component temporary value.
  • the determination of the model parameters in the embodiments of the present application not only considers the first image component neighboring reference value and the second image component neighboring reference value, but also considers the first image component reconstruction value; wherein, according to The degree of similarity between the reconstructed value of the first image component and the adjacent reference value of the first image component can obtain the matching pixel point of each pixel in the coding block, and the temporary value of the second image component is corresponding to the matched pixel point.
  • the second image component neighboring reference value is obtained; that is, the second image component temporary value is based on the first pixel corresponding to at least one pixel of the coding block in the coding block adjacent reference pixel corresponding to the first pixel
  • Two image components are obtained by adjacent reference values.
  • the reconstruction value according to the first image component, the first image component neighboring reference value, and the second The adjacent reference values of the image components to obtain the temporary value of the second image components include:
  • the second image component adjacent reference value corresponding to the matching pixel point is used as the temporary value of the second image component corresponding to each pixel point.
  • the The result of the difference calculation, and obtaining a matching pixel point of each pixel point from the adjacent reference pixels of the coding block includes:
  • the adjacent pixel points are used as matching pixel points of each pixel point.
  • the The result of the difference calculation, and obtaining a matching pixel point of each pixel point from the adjacent reference pixels of the coding block includes:
  • the temporary value of the second image component may be obtained by using a construction method such as interpolation. Therefore, in another possible implementation manner, the first image component reconstruction value, the first image component adjacent reference value, and the second image component adjacent reference value are used to obtain a first Two image component temporary values, including:
  • first matching pixel point represents the first matching pixel point An adjacent pixel point corresponding to a first image component adjacent reference value that is greater than the first image component reconstruction value and has the smallest difference among adjacent reference values of the image component
  • second matching pixel point represents the first An adjacent pixel point corresponding to a first image component adjacent reference value of an image component adjacent reference value that is smaller than the first image component reconstruction value and has the smallest difference
  • the search can be performed from the first image component neighboring reference value of the coding block, and first, a value greater than And with The closest reference value Y 1 of the first image component, the adjacent pixel point corresponding to Y 1 is the first matching pixel point, and the second reference value of the second image component corresponding to the first matching pixel point is C 1 ; Get a less than And with The closest reference value Y 2 of the first image component, the adjacent pixel point corresponding to Y 2
  • the position coordinate is the temporary value of the second image component corresponding to the [i, j] pixel point, which is represented by C '[i, j]; when the temporary value of the second image component of all pixels of the coding block has been found, you can use Instead Calculate the model parameters and use the model parameters to construct the predicted value of the second image component.
  • a set of original values of the second image component corresponding to the coding block can also be constructed.
  • the temporary value of the second image component for obtaining the temporary value of the second image component, not only the temporary value of the second image component can be obtained according to a matching method using the closest pixel, but also the temporary value of the second image component can be obtained by using an interpolation method. It is even possible to use the matching method of the closest pixel to some pixels and the interpolation method to obtain the temporary value of the second image component together; the embodiment of the present application does not specifically limit it.
  • the search range of the matching pixel point can also be expanded or reduced.
  • the search range can be limited to a row and column that differs from the coordinate position of the pixel point corresponding to the temporary value of the second image component to be determined by no more than n, where n is an integer greater than 1; the search range can also be extended to adjacent m rows And / or pixel position information of the m column, m is an integer greater than 1; pixel position information of other coding blocks in the lower left or upper right region may also be used; the embodiment of the present application does not specifically limit it.
  • the model parameters may be determined; in a possible implementation manner, the reconstructed value and the first image component according to the obtained first image component and the The obtained temporary value of the second image component and determining the model parameters include:
  • the first model parameter, the first image component reconstruction value, and the second image component temporary value are input into a second preset factor calculation model to obtain a second model parameter.
  • model parameters include the first model parameter and the second model parameter; after all the temporary image component corresponding values of the coding block are obtained, linear regression is still performed using the least square method to obtain the first parameter in the preset model.
  • a model parameter ⁇ 'and a second model parameter ⁇ ' are as follows:
  • the predicted value of the second image component corresponding to each pixel in the coding block can be obtained according to the established preset model; therefore, in the above implementation manner, Specifically, obtaining the predicted value of the second image component corresponding to each pixel in the coding block according to the model parameter includes:
  • a preset model is established based on the first model parameter and the second model parameter, wherein the preset model is used to characterize a first image component reconstruction value corresponding to each pixel point in the coding block and a second image component reconstruction value.
  • a second image component prediction value corresponding to each pixel point in the coding block is obtained.
  • a second image component prediction value Pred C [i, j] corresponding to a position coordinate of [i, j] pixels can be obtained.
  • a second image component temporary value is constructed to replace the original value of the second image component corresponding to the coding block, and then the first model is calculated according to the reconstructed value of the first image component and the original value of the second image component.
  • Parameters and second model parameters, and using the first model parameters and the second model parameters to establish a preset model can make the deviation of the established preset model from the expected model smaller, so that the second The image component prediction value is closer to the original value of the second image component, thereby improving the prediction accuracy of the video image component.
  • the obtained temporary value of the second image component may also be directly As the second image component prediction value, this embodiment of the present application does not specifically limit this. If the temporary value of the second image component is directly used as the prediction value of the second image component, the calculation of the first model parameter, the second model parameter, and the establishment of a preset model is not required at this time, which greatly reduces the calculation amount of the second image component prediction. .
  • the method further includes:
  • the third image component neighboring reference value represents a third image component reference value corresponding to each neighboring pixel point in the neighboring reference pixels of the coding block;
  • a third image component prediction value corresponding to each pixel in the coding block is obtained.
  • the determining a sub-model parameter according to the acquired second image component reconstruction value, the second image component neighboring reference value, and the third image component neighboring reference value include:
  • the embodiment of the present application in addition to the prediction of the first image component to the second image component, the second image component to the third image component, or the third image component to the second image component may be performed. Prediction of image components; wherein the prediction method from the third image component to the second image component is similar to the prediction method from the second image component to the third image component, the embodiment of the present application will use the prediction of the second image component to the third image component
  • the embodiment of the present application will use the prediction of the second image component to the third image component.
  • the same method as used for determining the temporary value of the second image component is adopted.
  • the closest pixel matching method may be used, or the interpolation method may also be used, so as to obtain the temporary value of the third image component.
  • the determined sub-model parameters include a first sub-model parameter and a second sub-model parameter; it can be established according to the first sub-model parameter and the second sub-model parameter.
  • a sub-preset model; according to the sub-preset model and a second image component reconstruction value corresponding to each pixel in the coding block, a third image component prediction corresponding to each pixel in the coding block can be obtained value.
  • the first sub-model parameter ⁇ * and the second sub-model parameter ⁇ * in the sub-preset model are as follows:
  • a sub-preset model After obtaining the first sub-model parameter ⁇ * and the second sub-model parameter ⁇ * , a sub-preset model can be established.
  • the established sub-preset model is shown in equation (9),
  • the above-mentioned prediction method applied to the CCLM prediction mode is also applicable to the MMLM prediction mode; as the name implies, the MMLM prediction mode has multiple preset models to realize the prediction of the second image component from the first image component; therefore Based on the technical solution shown in FIG. 8, in a possible implementation manner, the method further includes:
  • the model parameters are determined separately according to each of the at least two sets of the first image component reconstruction values and the second image component temporary values to obtain at least two sets of model parameters.
  • obtaining the predicted value of the second image component corresponding to each pixel in the coding block according to the model parameter includes:
  • a second image component prediction value corresponding to each pixel in the encoding block is obtained.
  • the threshold is the classification basis of the reconstructed value of the first image component of the coding block.
  • the threshold is a setting value used to indicate the establishment of multiple preset models.
  • the size of the threshold is related to the reconstruction value of the first image component corresponding to the coding block. Specifically, the first image component corresponding to the coding block may be calculated. The average value of the reconstruction values may also be obtained by calculating the median value of the reconstruction value of the first image component corresponding to the coding block, which is not specifically limited in this embodiment of the present application.
  • the mean Mean is calculated according to the reconstruction value of the first image component corresponding to at least one pixel of the coding block and Equation (10):
  • Mean represents the average value of the reconstructed value of the first image component corresponding to the coding block
  • M represents the number of samples of the reconstruction values of the first image component corresponding to the coding block.
  • the mean Mean can be directly used as a threshold, and two preset models can be established by using the threshold; however, it should be noted that the embodiment of the present application is not limited to only establishing two preset models.
  • a first model parameter ⁇ 1 ′ of a first preset model may be derived respectively And the second model parameter ⁇ 1 ′ and the first model parameter ⁇ 2 ′ and the second model parameter ⁇ 2 ′ of the second preset model; in combination with formula (11), a first preset model M1 ′ and a second preset are established Model M2 ':
  • the first preset model M1 'and the second preset model M2' After obtaining the first preset model M1 'and the second preset model M2', reconstruct the first image component value of each pixel in the coding block Compare with Threshold, if Then, the first preset model M1 'is selected, and according to the first preset model, the second image component prediction value Pred 1C [i, j] corresponding to the pixel coordinates of the coding block position [i, j] is obtained; Then, a second preset model M2 'is selected, and a second image component prediction value Pred 2C [i, j] corresponding to a pixel point coordinate of [i, j] is obtained according to the second preset model.
  • the reconstruction value of the first image component is compared with the mean value Mean, and Mean is used as a dividing point. If the reconstruction value of an image component is less than or equal to the threshold, it is divided into the first group; if the reconstruction value of the first image component is greater than the threshold, it is divided into the second group; in this way, the first image component reconstruction value set and the second group of the first group can be obtained.
  • the first image component of the group reconstructs a set of values.
  • the median calculation can also be performed for the first image component reconstruction value set of the first group to obtain the median value of the first group (requires The explanation is that the reconstruction value corresponding to only one pixel point in the first group is left); then the median calculation is continued for the first image component reconstruction value set of the second group to obtain the median value of the second group (which needs to be explained) (Yes, there is also a reconstruction value corresponding to only one pixel in the second group); according to the value corresponding to only one pixel in the first group and the value corresponding to only one pixel in the second group, these two can be obtained
  • the reconstructed values of the first image component and the temporary values of the second image component respectively corresponding to the pixels, so that the model parameters can be determined, and a preset model is established according to the model parameters, and the second image component can be obtained according to the established preset model.
  • Prediction value this prediction method can also greatly reduce the
  • This embodiment provides a method for predicting a video image component by acquiring a first image component reconstruction value, a first image component adjacent reference value, and a second image component adjacent reference value corresponding to a coding block;
  • An image component reconstruction value represents the reconstruction value of at least one pixel point of the coding block corresponding to the first image component, and the first image component neighboring reference value and the second image component neighboring reference value respectively represent the coding block.
  • Each adjacent pixel point in the adjacent reference pixel corresponds to the reference value of the first image component and the reference value of the second image component; according to the acquired first image component reconstruction value, the first image component adjacent reference value Determine a model parameter adjacent to the second image component reference value; obtain a predicted value of the second image component corresponding to each pixel in the coding block according to the model parameter; in the embodiment of the present application, the model parameter
  • FIG. 9 it illustrates a composition of a video image component prediction device 90 provided in an embodiment of the present application.
  • the video image component prediction device 90 may include: an obtaining section 901 , A determination section 902 and a prediction section 903;
  • the obtaining section 901 is configured to obtain a first image component reconstruction value, a first image component adjacent reference value, and a second image component adjacent reference value corresponding to a coding block; wherein the first image component reconstruction value
  • the at least one pixel point of the coding block corresponds to the reconstruction value of the first image component
  • the first image component neighboring reference value and the second image component neighboring reference value respectively characterize each of the coding block neighboring reference pixels
  • Adjacent pixel points correspond to the reference value of the first image component and the reference value of the second image component;
  • the determining section 902 is configured to determine a model parameter according to the acquired first image component reconstruction value, the first image component neighboring reference value, and the second image component neighboring reference value;
  • the prediction section 903 is configured to obtain a prediction value of a second image component corresponding to each pixel in the coding block according to the model parameter.
  • the obtaining section 901 is further configured to obtain a second image according to the first image component reconstruction value, the first image component neighboring reference value, and the second image component neighboring reference value.
  • Component temporary value wherein the second image component temporary value represents a temporary value of at least one pixel of the coding block corresponding to the second image component;
  • the determining section 902 is configured to determine a model parameter according to the first image component reconstruction value and the acquired second image component temporary value.
  • the video image component prediction device 90 further includes a calculation section 904, where:
  • the calculation section 904 is configured to perform, for each pixel point of the encoding block, any one of the first image component neighboring reference values and a first image component reconstruction value corresponding to each pixel point. Difference calculation
  • the obtaining section 901 is further configured to obtain a matching pixel point of each pixel point from neighboring reference pixels of the coding block according to a result of the difference calculation; and a second image corresponding to the matching pixel point.
  • the component neighboring reference value is used as the temporary value of the second image component corresponding to each pixel.
  • the obtaining section 901 is configured to obtain, according to a result of the difference calculation, an adjacent pixel point corresponding to a first image component adjacent reference value with the smallest difference; and the adjacent pixel The points serve as matching pixels for each pixel.
  • the obtaining section 901 is configured to obtain, according to a result of the difference calculation, a set of adjacent pixel points corresponding to the first reference value of the first image component with the smallest difference;
  • the calculation section 904 is further configured to calculate a distance value between each adjacent pixel point in the adjacent pixel point set and each of the pixel points;
  • the obtaining section 901 is further configured to select the adjacent pixel point with the smallest distance value as a matching pixel point of each pixel point.
  • the calculation section 904 is configured to, for each pixel point of the coding block, compare any reference value of the first image component neighboring reference value with the first value corresponding to each pixel point.
  • the obtaining section 901 is further configured to obtain a first matching pixel point and a second matching pixel point of each pixel point from neighboring reference pixels of the coding block according to a result of the difference calculation; wherein, the The first matching pixel point indicates an adjacent pixel point corresponding to a first image component adjacent reference value that is greater than the first image component reconstruction value and has the smallest difference among the first image component adjacent reference values.
  • the second matching pixel point represents an adjacent pixel point corresponding to the first image component adjacent reference value that is smaller than the first image component reconstruction value and has the smallest difference among the first image component adjacent reference values; and according to Performing a interpolation operation on a second image component neighboring reference value corresponding to the first matching pixel point and a second image component neighboring reference value corresponding to the second matching pixel point to obtain a second image corresponding to each pixel point Component temporary value.
  • the model parameters include a first model parameter and a second model parameter
  • the obtaining section 901 is further configured to input the first image component reconstruction value and the second image component temporary value to a first Obtaining a first model parameter in a preset factor calculation model
  • the obtaining section 901 is further configured to input the first model parameter, the first image component reconstruction value, and the second image component temporary value into a second preset factor calculation model to obtain the second Model parameters.
  • the video image component prediction device 90 further includes a establishing section 905, where:
  • the establishing section 905 is configured to establish a preset model based on the first model parameter and the second model parameter; wherein the preset model is used to characterize a first corresponding to each pixel in the coding block.
  • the prediction section 903 is further configured to obtain a second image component corresponding to each pixel in the encoding block according to the preset model and a first image component corresponding to each pixel in the encoding block. Predictive value.
  • the obtaining section 901 is further configured to obtain a second image component reconstruction value and a third image component neighboring reference value corresponding to the coding block; wherein the second image component reconstruction value represents the A second image component reconstruction value corresponding to at least one pixel point of the coding block, and the third image component neighboring reference value represents a third image component reference value corresponding to each neighboring pixel point in the neighboring reference pixels of the coding block;
  • the determining section 902 is further configured to determine a sub-model parameter according to the acquired second image component reconstruction value, the second image component neighboring reference value, and the third image component neighboring reference value;
  • the prediction section 903 is further configured to obtain a predicted value of a third image component corresponding to each pixel in the coding block according to the sub-model parameters.
  • the obtaining section 901 is configured to obtain a third image component according to the second image component reconstruction value, the second image component adjacent reference value, and the third image component adjacent reference value.
  • Temporary value wherein the third image component temporary value is obtained based on a third image component neighboring reference value corresponding to at least one pixel point of the coding block corresponding to a matching pixel point among neighboring reference pixels of the coding block ;
  • the determining section 902 is configured to determine a sub-model parameter according to the second image component reconstruction value and the acquired third image component temporary value.
  • the obtaining section 901 is further configured to obtain at least one threshold value based on a first image component reconstruction value corresponding to at least one pixel of the coding block; and according to the first image component reconstruction value and the first image component reconstruction value, Grouping comparison results of at least one threshold to obtain at least two sets of first image component reconstruction values and second image component temporary values; and according to each of the at least two sets of first image component reconstruction values and second image component temporary values One group determines the model parameters separately and obtains at least two sets of model parameters.
  • the establishing section 905 is further configured to establish at least two preset models based on the obtained at least two sets of model parameters; wherein the at least two preset models and the at least two sets of models Parameters have a corresponding relationship;
  • the prediction section 903 is further configured to select, from the at least two preset models, a pixel corresponding to each pixel in the coding block according to a comparison result between the first image component reconstruction value and the at least one threshold.
  • a preset model ; and obtaining a second image component prediction value corresponding to each pixel in the coding block according to a preset model corresponding to each pixel in the coding block and the first image component reconstruction value.
  • the “part” may be a part of a circuit, a part of a processor, a part of a program or software, etc., of course, it may be a unit, a module, or a non-modular.
  • each component in this embodiment may be integrated into one processing unit, or each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the above integrated unit may be implemented in the form of hardware or in the form of software functional modules.
  • the integrated unit is implemented in the form of a software functional module and is not sold or used as an independent product, it may be stored in a computer-readable storage medium.
  • the technical solution of this embodiment is essentially or It is said that a part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium and includes several instructions for making a computer device (can It is a personal computer, a server, or a network device) or a processor (processor) to perform all or part of the steps of the method described in this embodiment.
  • the foregoing storage media include: U disks, mobile hard disks, read only memories (ROM, Read Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.
  • this embodiment provides a computer storage medium that stores a prediction program for a video image component.
  • the prediction program for the video image component is executed by at least one processor, the technical solution shown in FIG. 8 is implemented. Steps of the method as described.
  • the composition of the prediction device 90 based on the video image component and the computer storage medium shown in FIG. 12, which shows a specific hardware structure of the prediction device 90 for a video image component provided in the embodiment of the present application, may include: a network interface 1201, a memory 1202 and processor 1203; the various components are coupled together through a bus system 1204. It can be understood that the bus system 1204 is configured to implement connection and communication between these components.
  • the bus system 1204 includes a power bus, a control bus, and a status signal bus in addition to the data bus. However, for the sake of clarity, various buses are marked as the bus system 1204 in FIG. 12.
  • the network interface 1201 is used to receive and send signals during the process of sending and receiving information with other external network elements.
  • the memory 1202 is configured to store a computer program capable of running on the processor 1203;
  • the processor 1203 is configured to, when running the computer program, execute:
  • first image component reconstruction value represents that at least one pixel of the coding block corresponds to A reconstruction value of a first image component, the first image component neighboring reference value and the second image component neighboring reference value respectively representing each neighboring pixel point in the coding block neighboring reference pixel corresponding to the first image
  • a second image component prediction value corresponding to each pixel in the coding block is obtained.
  • the memory 1202 in the embodiment of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), and an electronic memory. Erase programmable read-only memory (EPROM, EEPROM) or flash memory.
  • the volatile memory may be Random Access Memory (RAM), which is used as an external cache.
  • RAM Static Random Access Memory
  • DRAM Dynamic Random Access Memory
  • Synchronous Dynamic Random Access Memory Synchronous Dynamic Random Access Memory
  • SDRAM double data rate synchronous dynamic random access memory
  • Double Data Rate SDRAM DDRSDRAM
  • enhanced SDRAM ESDRAM
  • synchronous connection dynamic random access memory Synchronous DRAM, SLDRAM
  • Direct RAMbus RAM Direct RAMbus RAM, DRRAM
  • the memory 1202 of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.
  • the processor 1203 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method may be completed by an integrated logic circuit of hardware in the processor 1203 or an instruction in the form of software.
  • the above-mentioned processor 1203 may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a ready-made programmable gate array (Field Programmable Gate Array, FPGA), or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA ready-made programmable gate array
  • a general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the steps of the method disclosed in combination with the embodiments of the present application may be directly implemented by a hardware decoding processor, or may be performed by using a combination of hardware and software modules in the decoding processor.
  • the software module may be located in a mature storage medium such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, and the like.
  • the storage medium is located in the memory 1202, and the processor 1203 reads the information in the memory 1202 and completes the steps of the foregoing method in combination with its hardware.
  • the embodiments described herein may be implemented by hardware, software, firmware, middleware, microcode, or a combination thereof.
  • the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSP), digital signal processing devices (DSPD), programmable Logic device (Programmable Logic Device, PLD), Field Programmable Gate Array (FPGA), general purpose processor, controller, microcontroller, microprocessor, other for performing the functions described in this application Electronic unit or combination thereof.
  • ASICs application-specific integrated circuits
  • DSP digital signal processors
  • DSPD digital signal processing devices
  • PLD programmable Logic Device
  • FPGA Field Programmable Gate Array
  • controller microcontroller
  • microprocessor other for performing the functions described in this application Electronic unit or combination thereof.
  • the techniques described herein can be implemented through modules (e.g., procedures, functions, etc.) that perform the functions described herein.
  • Software codes may be stored in a memory and executed by a processor.
  • the memory may be implemented in the processor or external to the processor.
  • the processor 1203 is further configured to execute the steps of the method for predicting a video image component in the technical solution shown in FIG. 8 when the computer program is run.
  • the first image component reconstruction value, the first image component adjacent reference value, and the second image component adjacent reference value corresponding to the encoding block are obtained; wherein the first image component reconstruction value represents the encoding.
  • At least one pixel of the block corresponds to the reconstruction value of the first image component, and the first image component neighboring reference value and the second image component neighboring reference value respectively characterize each neighboring of the coding block neighboring reference pixels.
  • the pixels correspond to the reference value of the first image component and the reference value of the second image component; according to the acquired reconstruction value of the first image component, the first image component adjacent reference value, and the second image component adjacent
  • the reference value determines the model parameter; according to the model parameter, the second image component prediction value corresponding to each pixel in the coding block is obtained; thereby the prediction accuracy of the video image component can be effectively improved, and the video image component prediction value can be effectively improved It is closer to the original value of the video image component, which saves the coding rate.

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Abstract

本申请实施例公开了一种视频图像分量的预测方法、装置及计算机存储介质，该方法包括：获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值。

Description

视频图像分量的预测方法、装置及计算机存储介质 技术领域
本申请实施例涉及视频编解码的技术领域，尤其涉及一种视频图像分量的预测方法、装置及计算机存储介质。
背景技术
随着人们对视频显示质量要求的提高，高清和超高清视频等新视频应用形式应运而生。在这种高分辨率高质量视频欣赏应用越来越广泛的情况下，对视频压缩技术的要求也越来越高。H.265/高效率视频编码(High Efficiency Video Coding，HEVC)是目前最新的国际视频压缩标准，H.265/HEVC的压缩性能比前一代视频编码标准H.264/先进视频编码(Advanced Video Coding，AVC)提高约50％，但仍然满足不了视频应用迅速发展的需求，尤其是超高清、虚拟现实(Virtual Reality，VR)等新视频应用。
ITU-T的视频编码专家组和ISO/IEC的运动图像专家组于2015年成立联合视频研究组(Joint Video Exploration Team，JVET)着手制定下一代的视频编码标准。联合探索测试模型(Joint Exploration Test Model，JEM)为通用的参考软件平台，不同编码工具基于此平台验证。2018年4月，JVET正式命名下一代视频编码标准为多功能视频编码(Versatile Video Coding，VVC)，其相应的测试模型为VTM。在JEM和VTM参考软件中，已经集成了一种基于线性模型的预测方法，色度分量可以通过线性模型由亮度分量得到色度预测值。然而，在构建线性模型时，计算出的色度预测值准确度偏低。
发明内容
为解决上述技术问题，本申请实施例期望提供一种视频图像分量的预测方法、装置及计算机存储介质，能够有效提高视频图像分量的预测准确度，使得视频图像分量预测值更加接近于视频图像分量原始值，进而节省了编码码率。
本申请实施例的技术方案可以如下实现：
第一方面，本申请实施例提供了一种视频图像分量的预测方法，所述方法包括：
获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；
根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；
根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值。
第二方面，本申请实施例提供了一种视频图像分量的预测装置，所述视频图像分量的预测装置包括：获取部分、确定部分和预测部分；
所述获取部分，配置为获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图 像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；
所述确定部分，配置为根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；
所述预测部分，配置为根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值。
第三方面，本申请实施例提供了一种视频图像分量的预测装置，所述视频图像分量的预测装置包括：存储器和处理器；
所述存储器，用于存储能够在所述处理器上运行的计算机程序；
所述处理器，用于在运行所述计算机程序时，执行第一方面所述的方法的步骤。
第四方面，本申请实施例提供了一种计算机存储介质，所述计算机存储介质存储有视频图像分量的预测程序，所述视频图像分量的预测程序被至少一个处理器执行时实现第一方面所述的方法的步骤。
本申请实施例提供了一种视频图像分量的预测方法、装置及计算机存储介质，通过获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值；由于本申请实施例中模型参数的确定不仅考虑了第一图像分量相邻参考值和第二图像分量相邻参考值，而且还考虑了第一图像分量重建值，可以使得第二图像分量预测值更加接近于第二图像分量原始值，从而能够有效提高视频图像分量的预测准确度，使得视频图像分量预测值更加接近于视频图像分量原始值，进而节省了编码码率。
附图说明
图1A至图1C为相关技术方案中视频图像采样格式的结构示意图；
图2A和图2B为相关技术方案中编码块的第一图像分量相邻参考值和第二图像分量相邻参考值的采样示意图；
图3A至图3C为相关技术方案中CCLM预设模型的原理结构示意图；
图4为相关技术方案中MMLM预测模式中第一图像分量相邻参考值和第二图像分量相邻参考值的分组示意图；
图5为本申请实施例提供的一种编码块中每个像素点以及相邻参考像素的分布示意图；
图6为本申请实施例提供的一种视频编码系统的组成框图示意图；
图7为本申请实施例提供的一种视频解码系统的组成框图示意图；
图8为本申请实施例提供的一种视频图像分量的预测方法流程示意图；
图9为本申请实施例提供的一种视频图像分量的预测装置的组成结构示意图；
图10为本申请实施例提供的另一种视频图像分量的预测装置的组成结构示意图；
图11为本申请实施例提供的又一种视频图像分量的预测装置的组成结构示意图；
图12为本申请实施例提供的一种视频图像分量的预测装置的具体硬件结构示意图。
具体实施方式
为了能够更加详尽地了解本申请实施例的特点与技术内容，下面结合附图对本申请实施例的实现进行详细阐述，所附附图仅供参考说明之用，并非用来限定本申请实施例。
在视频图像中，一般采用第一图像分量、第二图像分量和第三图像分量来表征编码块；其中，这三个图像分量分别为一个亮度分量、一个蓝色色度分量和一个红色色度分量，具体地，亮度分量通常使用符号Y表示，蓝色色度分量通常使用符号Cb表示，红色色度分量通常使用符号Cr表示。
在本申请实施例中，第一图像分量可以为亮度分量Y，第二图像分量可以为蓝色色度分量Cb，第三图像分量可以为红色色度分量Cr，但是本申请实施例对此不作具体限定。目前常用的采样格式为YCbCr格式，YCbCr格式包括以下几种，分别如图1A至图1C所示，其中，图中的叉(X)表示第一图像分量采样点，圈(○)表示第二图像分量或第三图像分量采样点。YCbCr格式包括：
4:4:4格式：如图1A所示，表示第二图像分量或第三图像分量没有下采样；它是在每个扫描行上每4个连续的像素点取4个第一图像分量的采样样本、4个第二图像分量的采样样本和4个第三图像分量的采样样本；
4:2:2格式：如图1B所示，表示第一图像分量相对于第二图像分量或第三图像分量进行2:1的水平采样，没有竖直下采样；它是在每个扫描行上每4个连续的像素点取4个第一图像分量的采样样本、2个第二图像分量的采样样本和2个第三图像分量的采样样本；
4:2:0格式：如图1C所示，表示第一图像分量相对于第二图像分量或第三图像分量进行2:1的水平下采样和2:1的竖直下采样；它是在水平扫描行和垂直扫描行上每2个连续的像素点取2个第一图像分量的采样样本、1个第二图像分量的采样样本和1个第三图像分量的采样样本。
在视频图像采用YCbCr为4:2:0格式的情况下，若视频图像的第一图像分量为2N×2N大小的编码块，则对应的第二图像分量或第三图像分量为N×N大小的编码块，其中N为编码块的边长。在本申请实施例中，下述将以4:2:0格式为例进行描述，但是本申请实施例的技术方案同样适用于其他采样格式。
在下一代视频编码标准H.266中，为了进一步提升了编码性能和编码效率，针对分量间预测(Cross-component Prediction，CCP)进行了扩展改进，提出了分量间线性模型预测(Cross-component Linear Model Prediction，CCLM)。在H.266中，CCLM实现了第一图像分量到第二图像分量、第一图像分量到第三图像分量以及第二图像分量与第三图像分量之间的预测，下述将以第一图像分量到第二图像分量的预测为例进行描述，但是本申请实施例的技术方案同样也可以适用于其他图像分量的预测。
可以理解地，为了减少第一图像分量与第二图像分量之间的冗余，在使用CCLM的预测模式中，第一图像分量和第二图像分量为同一编码块的，该第二图像分量是基于同一编码块的第一图像分量重建值进行预测的，例如使用如式(1)所示的预设模型：
Pred C[i,j]＝α·Rec Y[i,j]+β           (1)
其中，i，j表示编码块中采样点的位置坐标，i表示水平方向，j表示竖直方向，Pred C[i,j]表示编码块中位置坐标为[i,j]的采样点对应的第二图像分量预测值，Rec Y[i,j]表示同一编码块中(经过下采样的)位置坐标为[i,j]的采样点对应的第一图像分量重建值，α和β是上述预设模型的模型参数，可以通过编码块周围的第一图像分量相邻参考值和第二图像分量相邻参考值的最小化回归误差推导得到，如使用式(2)计算得到：
Figure PCTCN2018107109-appb-000001
其中，Y(n)表示经过下采样的左侧和上侧所有第一图像分量相邻参考值，C(n)表示左侧和上侧所有第二图像分量相邻参考值，N为第二图像分量编码块的边长，n＝1,2,...,2N。参见图2A和图2B，其分别示出了相关技术方案中编码块的第一图像分量相邻参考值和第二图像分量相邻参考值的采样示意图；其中，在图2A中，加粗的较大方框用于突出指示第一图像分量编码块21，而灰色实心圆圈用于指示第一图像分量编码块21的相邻参考值Y(n)；在图2B中，加粗的较大方框用于突出指示第二图像分量编码块22，而灰色实心圆圈用于指示第二图像分量编码块22的相邻参考值C(n)。图2A示出了2N×2N大小的第一图像分量编码块21，对于4:2:0格式的视频图像来说，一个2N×2N大小的第一图像分量相对应的第二图像分量的大小为N×N，如图2B中的22所示；也就是说，图2A和图2B是针对同一编码块分别进行第一图像分量采样和第二图像分量采样所得到的编码块示意图。这里，对于方形编码块，式(2)可以直接应用；对于非方形的编码块，较长边缘的邻近采样首先进行下采样，得到与较短边缘采样数量相等的采样数。α和β并不需要传输，在解码器中也可以通过式(2)计算得到；在本申请实施例中，对此不作具体限定。
图3A至图3C示出了相关技术方案中CCLM预设模型的原理结构示意图，如图3A至图3C所示，a、b、c为第一图像分量相邻参考值，A、B、C为第二图像分量相邻参考值，e为编码块中某像素点对应的第一图像分量重建值，E为该像素点对应的第二图像分量预测值；其中，利用编码块所有的第一图像分量相邻参考值Y(n)和第二图像分量相邻参考值C(n)，根据式(2)可以计算得出α和β，根据计算得到的α和β以及式(1)可以建立预设模型，如图3C所示；将编码块中某像素点对应的第一图像分量重建值e带入式(1)所述的预设模型中，可以计算得到该像素点对应的第二图像分量预测值E。
在JEM中，目前有两种CCLM的预测模式：一种是单模型CCLM的预测模式；另一种是多模型CCLM(Multiple Model CCLM，MMLM)的预测模式，也称为MMLM的预测模式。顾名思义，单一模型CCLM的预测模式则是只有一种预设模型来实现由第一图像分量预测第二图像分量；而MMLM的预测模式则是有多个预设模型来实现由第一图像分量预测第二图像分量。例如，在MMLM的预测模式中，将编码块的第一图像分量相邻参考值和第二图像分量相邻参考值分成两组，每一组均可以单独作为推导预设模型中模型参数的训练集，即每一个分组都能推导出一组模型参数α和β；而且编码块的第一图像分量重建值也可以根据第一图像分量相邻参考值的分类方法进行分组，并分别使用对应的模型参数α和β来建立预设模型。
图4示出了相关技术方案中MMLM预测模式中第一图像分量相邻参考值和第二图像分量相邻参考值的分组示意图；其中，阈值是用于指示建立多个预设模型所依据的设定值，阈值的大小是根据第一图像分量相邻参考值Y(n)进行求均值得到；从图4中可以看到，假定阈值用Threshold表示，以Threshold为分界点，若第一图像分量相邻参考值小于等于阈值，则划分到第一组；若第一图像分量相邻参考值大于阈值，则划分到第二组；这里，根据第一组的第一图像分量相邻参考值和第二图像分量相邻参考值可以推导得到第一预设模型的模型参数α1和β1，比如α1＝2，β1＝1；根据第二组的第一图像分量相邻参考值和第二图像分量相邻参考值可以推导得到第二预设模型的模型参数α2和 β2，比如α2＝1/2，β2＝-1；所建立的第一预设模型M1和第二预设模型M2如式(3)所示，
Figure PCTCN2018107109-appb-000002
其中，Rec Y[i,j]表示编码块中位置坐标为[i,j]像素点对应的第一图像分量重建值；Pred 1C[i,j]表示编码块中位置坐标为[i,j]像素点根据第一预设模型M1所得到的第二图像分量预测值，Pred 2C[i,j]表示编码块中位置坐标为[i,j]像素点根据第二预设模型M2所得到的第二图像分量预测值。
现有技术方案是利用编码块的第一图像分量相邻参考值和第二图像分量相邻参考值来计算得出预设模型的模型参数α和β；具体地，α和β是通过最小化第一图像分量相邻参考值和第二图像分量相邻参考值的回归误差得到，参见上述式(2)所示。
然而，由于空间区域的不同，图像空间纹理往往会发生变化，不同区域像素点的分布特性不同，比如部分像素点为高亮度，部分像素点为低亮度；如果只是简单的使用相邻参考像素来构造预设模型的模型参数，由于考虑不够全面，所构造的模型参数不是最优的，使得通过该预设模型所得到的第二图像分量预测值不够精确。
在本申请实施例中，视频图像分量的预测方法提出基于编码块的第一图像分量重建值和第二图像分量临时值来构造预设模型的模型参数，其中，第二图像分量临时值则是根据编码块的第一图像分量重建值和第一图像分量相邻参考值之间的相似程度获得的，使得所构造的模型参数尽可能贴近于最优模型参数；参见图5，其示出了本申请实施例提供的一种编码块中每个像素点以及相邻参考像素的分布示意图；如图5所示，编码块的相邻参考像素以高亮度为主，而编码块中像素点以中低亮度为主；由于本申请实施例不仅考虑了相邻参考像素对应的第一图像分量相邻参考值和第二图像分量相邻参考值，同时还考虑了第一图像分量重建值和第一图像分量相邻参考值之间的相似程度，使得所构造的模型参数尽可能贴近于最优模型参数。在本申请实施例中，编码块的第一图像分量重建值不仅参与了预设模型的应用，还参与了模型参数的计算，进一步使得通过本申请实施例所得到的第二图像分量预测值更加接近于第二图像分量原始值。下面将结合附图，对本申请实施例的技术方案进行清楚、完整地描述。
参见图6，其示出了一种视频编码系统的组成框图示例；如图6所示，该视频编码系统600包括变换与量化601、帧内估计602、帧内预测603、运动补偿604、运动估计605、反变换与反量化606、滤波器控制分析607、去方块滤波及样本自适应缩进(Sample Adaptive 0ffset，SAO)滤波608、头信息编码及基于上下文的自适应二进制算术编码(Context-based Adaptive Binary Arithmatic Coding，CABAC)609和解码图像缓存610等部件；针对输入的原始视频信号，通过编码树块(Coding Tree Unit，CTU)的划分可以得到一个视频编码块，然后通过变换与量化601对该视频编码块进行变换，包括将残差信息从像素域变换到变换域，并对所得的变换系数进行量化，用以进一步减少位率；帧内估计602和帧内预测603是用于对该视频编码块进行帧内预测；明确地说，帧内估计602和帧内预测603用于确定待用以编码该视频编码块的帧内预测模式；运动补偿604和运动估计605用于执行所接收的视频编码块相对于一或多个参考帧中的一或多个块的帧间预测编码以提供时间预测；由运动估计605执行的运动估计为产生运动向量的过程，所述运动向量可以估计该视频编码块的运动，然后由运动补偿604基于由运动估计605所确定的运动向量执行运动补偿；在确定帧内预测模式之后，帧内预测603还用于将所选择的帧内预测数据提供到头信息编码及CABAC 609，而且运动估计605将所计算确定的运动向量数据也发送到头信息编码及CABAC 609；此外，反变换与反量化606是 用于该视频编码块的重构建，在像素域中重构建残差块，该重构建残差块通过滤波器控制分析607和去方块滤波及SAO滤波608去除方块效应伪影，然后将该重构残差块添加到解码图像缓存610的帧中的一个预测性块，用以产生经重构建的视频编码块；头信息编码及CABAC 609是用于编码量化后的变换系数，在基于CABAC的编码算法中，上下文内容可基于相邻编码块，可用于编码指示所确定的帧内预测模式的信息，输出该视频信号的码流；而解码图像缓存610是用于存放重构建的视频编码块，随着视频图像编码的进行，会不断生成新的重构建的视频编码块，这些重构建的视频编码块都会被存放在解码图像缓存610中。
参见图7，其示出了一种视频解码系统的组成框图示例；如图7所示，该视频解码系统700包括头信息解码及CABAC解码701、反变换与反量化702、帧内预测703、运动补偿704、去方块滤波及SAO滤波705和解码图像缓存706等部件；输入的视频信号经过图6的编码处理之后，输出该视频信号的码流；该码流输入视频解码系统700中，首先经过头信息解码及CABAC解码701，用于得到解码后的变换系数；针对该变换系数通过反变换与反量化702进行处理，以便在像素域中产生残差块；帧内预测703可用于基于所确定的帧内预测模式和来自当前帧或图片的先前经解码块的数据而产生当前视频解码块的预测数据；运动补偿704是通过剖析运动向量和其他关联语法元素来确定用于视频解码块的预测信息，并使用该预测信息以产生正被解码的视频解码块的预测性块；通过对来自反变换与反量化702的残差块与由运动补偿704产生的对应预测性块进行求和，而形成解码的视频块；该解码的视频信号通过去方块滤波及SAO滤波705以便去除方块效应伪影，可以改善视频质量；然后将经解码的视频块存储于解码图像缓存706中，解码图像缓存706存储用于后续运动补偿的参考图像，也存储经过解码所输出的视频信号，即得到了所恢复的原始视频信号。
本申请实施例主要应用在如图6所示的帧内预测603部分和如图7所示的帧内预测703部分；也就是说，本申请实施例对于编码系统和解码系统可以同时作用，本申请实施例对此不作具体限定。
基于上述图6或者图7的应用场景示例，参见图8，其示出了本申请实施例提供的一种视频图像分量的预测方法流程，所述方法可以包括：
S801：获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；
S802：根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；
S803：根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值。
需要说明的是，编码块为待进行第二图像分量预测或者第三图像分量预测的当前编码块；第一图像分量重建值用于表征编码块中至少一个像素点所对应的第一图像分量的重建值，第一图像分量相邻参考值用于表征编码块相邻参考像素点所对应的第一图像分量的参考值，第二图像分量相邻参考值用于表征编码块相邻参考像素点所对应的第二图像分量的参考值。
在图8所示的技术方案中，通过获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所 述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值；在本申请实施例中模型参数的确定不仅考虑了第一图像分量相邻参考值和第二图像分量相邻参考值，而且还考虑了第一图像分量重建值，可以使得第二图像分量预测值更加接近于第二图像分量原始值，从而能够有效提高视频图像分量的预测准确度，使得视频图像分量预测值更加接近于视频图像分量原始值，进而节省了编码码率。
可以理解地，从数学理论角度对获取预设模型的最优模型参数进行分析；对于编码块中每个像素点对应的第二图像分量预测值，一般都会希望第二图像分量原始值与第二图像分量预测值之间的预测残差越小越好，故优化的目标函数为
Figure PCTCN2018107109-appb-000003
其中，i、j表示所述编码块中像素点的位置坐标，i表示水平方向，j表示竖直方向，
Figure PCTCN2018107109-appb-000004
为编码块中位置坐标为[i,j]的像素点对应的第一图像分量重建值，C[i,j]为编码块中位置坐标为[i,j]的像素点对应的第二图像分量原始值，C Pred[i,j]为编码块中位置坐标为[i,j]的像素点对应的第二图像分量预测值；这里，预设模型的最优模型参数α opt和β opt可由最小二乘法求得，如式(5)所示，
Figure PCTCN2018107109-appb-000005
从式(5)中可以看出，利用编码块内N×N个第一图像分量重建值
Figure PCTCN2018107109-appb-000006
和第二图像分量原始值C[i,j]进行线性回归，可以得到均方误差最小条件下的最优模型参数α opt和β opt。
然而，由于最优模型参数α opt和β opt的计算使用了编码块的第二图像分量原始值，但是第二图像分量原始值在解码端无法得到，因此需将α opt和β opt进行传输，这种情况下会引入额外的比特开销；另外，模型参数α opt和β opt的绝对值幅度大且跨度大，如果对其进行传输，对预测性能而言可能得不偿失。因此，在本申请实施例中，基于编码块的第一图像分量重建值与第一图像分量相邻参考值之间的相似程度来构造第二图像分量临时值，用所构造的第二图像分量临时值来替代编码块中每个像素点对应的第二图像分量原始值；这种情况下既可以得到最优模型参数，还不会引入额外的比特开销。
基于图8所示的技术方案，在一种可能的实现方式中，所述根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数，包括：
根据所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，获取第二图像分量临时值；其中，所述第二图像分量临时值表征所述编码 块至少一个像素点对应第二图像分量的临时值；
根据所述第一图像分量重建值和所述获取的第二图像分量临时值，确定模型参数。
需要说明的是，本申请实施例中模型参数的确定，不仅考虑了第一图像分量相邻参考值和第二图像分量相邻参考值，而且还考虑了第一图像分量重建值；其中，根据第一图像分量重建值与第一图像分量相邻参考值之间的相似程度，可以获取到编码块中每个像素点的匹配像素点，而第二图像分量临时值是根据匹配像素点所对应的第二图像分量相邻参考值得到的；也就是说，第二图像分量临时值是基于所述编码块至少一个像素点在所述编码块相邻参考像素中的匹配像素点所对应的第二图像分量相邻参考值得到的。
可以理解地，对于第二图像分量临时值的获取，在一种可能的实现方式中，所述根据所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，获取第二图像分量临时值，包括：
针对所述编码块每个像素点，将所述第一图像分量相邻参考值中任一个参考值与所述每个像素点对应的第一图像分量重建值进行差值计算；
根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的匹配像素点；
将所述匹配像素点对应的第二图像分量相邻参考值，作为每个像素点对应的第二图像分量临时值。
可选地，当根据差值计算的结果，得到差值最小的第一图像分量相邻参考值所对应的相邻像素点只有1个时，在上述实现方式中，具体地，所述根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的匹配像素点，包括：
根据所述差值计算的结果，获取差值最小的第一图像分量相邻参考值所对应的相邻像素点；
将所述相邻像素点作为每个像素点的匹配像素点。
可选地，当根据差值计算的结果，得到差值最小的第一图像分量相邻参考值所对应的相邻像素点具有多个时，在上述实现方式中，具体地，所述根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的匹配像素点，包括：
根据所述差值计算的结果，获取差值最小的第一图像分量相邻参考值所对应的相邻像素点集合；
计算所述相邻像素点集合中每个相邻像素点与所述每个像素点之间的距离值，并且选取所述距离值最小的相邻像素点作为每个像素点的匹配像素点。
需要说明的是，在CCLM的预测模式中，对于编码块位置坐标为[i,j]的像素点，假定已经得到该像素点对应的第一图像分量重建值
Figure PCTCN2018107109-appb-000007
从编码块的第一图像分量相邻参考值中搜索与
Figure PCTCN2018107109-appb-000008
最接近的第一图像分量相邻参考值，即两者之间的差值最小；若所搜索到的与
Figure PCTCN2018107109-appb-000009
最接近的第一图像分量相邻参考值对应的相邻像素点只有1个，则该相邻像素点就是位置坐标为[i,j]像素点的匹配像素点；若所搜索到的与
Figure PCTCN2018107109-appb-000010
最接近的第一图像分量相邻参考值对应的相邻像素点有多个，即得到相邻像素点集合，则还需要计算所述相邻像素点集合中每个相邻像素点与所述每个像素点之间的距离值，然后选取所述距离值最小的相邻像素点作为位置坐标为[i,j]像素点的匹配像素点；在得到匹配像素点之后，此时匹配像素点对应的第二图像分量就可以作为位置坐标为[i,j]像素点对应的第二图像分量临时值，用C'[i,j]表示；当编码块所有像素点的第二图像分量临时值均已找到，此时可以用
Figure PCTCN2018107109-appb-000011
来代替
Figure PCTCN2018107109-appb-000012
计算模型参数， 并将该模型参数用于构造第二图像分量预测值；利用上述方法，可以构造出一个与编码块对应的第二图像分量原始值较为接近的一个集合。
可以理解地，对于第二图像分量临时值的获取，除了可以根据上述使用最接近像素的匹配方法来得到第二图像分量临时值之外，还可以利用插值等构造方法来得到第二图像分量临时值；因此，在另一种可能的实现方式中，所述根据所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，获取第二图像分量临时值，包括：
针对所述编码块每个像素点，将所述第一图像分量相邻参考值中任一个参考值与所述每个像素点对应的第一图像分量重建值进行差值计算；
根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的第一匹配像素点和第二匹配像素点；其中，所述第一匹配像素点表示所述第一图像分量相邻参考值中大于所述第一图像分量重建值且所述差值最小的第一图像分量相邻参考值对应的相邻像素点，所述第二匹配像素点表示所述第一图像分量相邻参考值中小于所述第一图像分量重建值且所述差值最小的第一图像分量相邻参考值对应的相邻像素点；
根据所述第一匹配像素点对应的第二图像分量相邻参考值和所述第二匹配像素点对应的第二图像分量相邻参考值并进行插值运算，得到每个像素点对应的第二图像分量临时值。
需要说明的是，在CCLM的预测模式中，对于编码块位置坐标为[i,j]的像素点，假定已经得到该像素点对应的第一图像分量重建值
Figure PCTCN2018107109-appb-000013
基于第一图像分量相邻参考值与第一图像分量重建值最为接近的原则，希望从第一图像分量相邻参考值中寻找到一个所需要的第一图像分量相邻参考值Y C，且希望所需要的
Figure PCTCN2018107109-appb-000014
但是从该第一图像分量相邻参考值中无法直接得到所需要的Y C；这时候可以从编码块的第一图像分量相邻参考值中进行搜索，首先得到一个大于
Figure PCTCN2018107109-appb-000015
并且与
Figure PCTCN2018107109-appb-000016
最为接近的第一图像分量相邻参考值Y 1，Y 1对应的相邻像素点为第一匹配像素点，该第一匹配像素点对应的第二图像分量相邻参考值为C 1；再得到一个小于
Figure PCTCN2018107109-appb-000017
并且与
Figure PCTCN2018107109-appb-000018
最为接近的第一图像分量相邻参考值Y 2，Y 2对应的相邻像素点为第二匹配像素点，该第二匹配像素点对应的第二图像分量相邻参考值为C 2；即所搜索得到的第一匹配像素点和第二匹配像素点就是位置坐标为[i,j]像素点的第一匹配像素点和第二匹配像素点；此时可以利用Y C、Y 1、C 1、Y 2和C 2进行插值运算，这里假定
Figure PCTCN2018107109-appb-000019
所得到与Y C对应的第二图像分量相邻参考值C C＝C 2+(Y C-Y 2)×(C 1-C 2)/(Y 1-Y 2)，该值就可以作为位置坐标为[i,j]像素点对应的第二图像分量临时值，用C'[i,j]表示；当编码块所有像素点的第二图像分量临时值均已找到，此时可以用
Figure PCTCN2018107109-appb-000020
来代替
Figure PCTCN2018107109-appb-000021
计算模型参数，并将该模型参数用于构造第二图像分量预测值；利用上述方法，也可以构造出一个与编码块对应的第二图像分量原始值较为接近的一个集合。
在本申请实施例中，对于第二图像分量临时值的获取，不仅可以根据使用最接近像素的匹配方法来得到第二图像分量临时值，还可以使用插值方法来得到第二图像分量临时值，甚至还可以使用部分像素采用最接近像素的匹配方法、部分像素采用插值方法共同来得到第二图像分量临时值；本申请实施例不作具体限定。
在本申请实施例中，对于第二图像分量临时值的获取，还可以扩大或缩小匹配像素点的查找范围。例如，可以将查找范围限定在与待确定第二图像分量临时值对应像素点 坐标位置的行与列相差不超过n，n为大于1的整数；也可以将查找范围扩展到相邻的m行和/或m列的像素点位置信息，m为大于1的整数；甚至还可以使用左下或者右上区域中其他编码块的像素点位置信息；本申请实施例不作具体限定。
可以理解地，在编码块对应的第二图像分量临时值全部得到之后，可以确定出模型参数；在一种可能的实现方式中，所述根据获取的所述第一图像分量重建值和所述获取的第二图像分量临时值，确定模型参数，包括：
将所述第一图像分量重建值和所述第二图像分量临时值输入至第一预设因子计算模型中，获得第一模型参数；
将所述第一模型参数、所述第一图像分量重建值和所述第二图像分量临时值输入至第二预设因子计算模型中，获得第二模型参数。
需要说明的是，模型参数包括第一模型参数和第二模型参数；在编码块对应的第二图像分量临时值全部得到之后，仍然利用最小二乘法进行线性回归，可以得到预设模型中的第一模型参数α'和第二模型参数β'如下：
Figure PCTCN2018107109-appb-000022
可以理解地，在得到第一模型参数和第二模型参数之后，可以根据所建立的预设模型来获取编码块中每个像素点对应的第二图像分量预测值；因此，在上述实现方式中，具体地，所述根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值，包括：
基于所述第一模型参数和所述第二模型参数，建立预设模型；其中，所述预设模型用于表征所述编码块中每个像素点对应的第一图像分量重建值与第二图像分量预测值之间的计算关系；
根据所述预设模型和所述编码块中每个像素点对应的第一图像分量重建值，得到所述编码块中每个像素点对应的第二图像分量预测值。
需要说明的是，根据所获取的第一模型参数α'和第二模型参数β'，所建立的预设模型如式(7)所示，
Figure PCTCN2018107109-appb-000023
对于位置坐标为[i,j]的像素点，根据已经获得的第一图像分量重建值
Figure PCTCN2018107109-appb-000024
结合如上式(7)所述的预设模型，可以得到位置坐标为[i,j]像素点对应的第二图像分量预测值Pred C[i,j]。
在本申请实施例中，通过构造第二图像分量临时值来替代编码块对应的第二图像分量原始值，然后根据第一图像分量重建值与第二图像分量原始值来计算得出第一模型参数和第二模型参数，将第一模型参数和第二模型参数用于建立预设模型，可以使得所建立的预设模型与期望模型的偏差较小，使得根据该预设模型得到的第二图像分量预测值更加贴近于第二图像分量原始值，从而提高了视频图像分量的预测准确度。
在本申请实施例中，除了根据上述第一模型参数、第二模型参数以及所建立的预设模型来获取第二图像分量预测值之外，还可以将所得到的第二图像分量临时值直接作为第二图像分量预测值；本申请实施例对此不作具体限定。如果直接用第二图像分量临时值直接作为第二图像分量预测值，此时不需要计算第一模型参数、第二模型参数以及建 立预设模型，大大减小了第二图像分量预测的计算量。
基于图8所示的技术方案，在一种可能的实现方式中，所述方法还包括：
获取所述编码块对应的第二图像分量重建值和第三图像分量相邻参考值；其中，所述第二图像分量重建值表征所述编码块至少一个像素点对应的第二图像分量重建值，所述第三图像分量相邻参考值表征所述编码块相邻参考像素中每个相邻像素点对应的第三图像分量参考值；
根据获取的所述第二图像分量重建值、所述第二图像分量相邻参考值和所述第三图像分量相邻参考值，确定子模型参数；
根据所述子模型参数，获取所述编码块中每个像素点对应的第三图像分量预测值。
在上述实现方式中，具体地，所述根据获取的所述第二图像分量重建值、所述第二图像分量相邻参考值和所述第三图像分量相邻参考值，确定子模型参数，包括：
根据所述第二图像分量重建值、所述第二图像分量相邻参考值和所述第三图像分量相邻参考值，获取第三图像分量临时值；其中，所述第三图像分量临时值是基于所述编码块至少一个像素点在所述编码块相邻参考像素中的匹配像素点所对应的第三图像分量相邻参考值得到的；
根据所述第二图像分量重建值和所述获取的第三图像分量临时值，确定子模型参数。
需要说明的是，在本申请实施例中，除了可以进行第一图像分量到第二图像分量的预测之外，还可以进行第二图像分量到第三图像分量、或者第三图像分量到第二图像分量的预测；其中，第三图像分量到第二图像分量的预测和第二图像分量到第三图像分量的预测方法相似，本申请实施例将以第二图像分量到第三图像分量的预测为例进行以下描述。
具体地，在获取到编码块的第二图像分量重建值和编码块的第三图像分量相邻参考值之后，结合第二图像分量相邻参考值，采用上述确定第二图像分量临时值的相同方法，比如可以采用最接近像素的匹配方法，或者也可以采用插值方法，从而可以得到第三图像分量临时值。这里，根据第二图像分量重建值和第三图像分量临时值，所确定的子模型参数包括第一子模型参数和第二子模型参数；根据第一子模型参数和第二子模型参数可以建立子预设模型；根据所述子预设模型和和所述编码块中每个像素点对应的第二图像分量重建值，可以得到所述编码块中每个像素点对应的第三图像分量预测值。
在本申请实施例中，假定编码块的第二图像分量重建值用
Figure PCTCN2018107109-appb-000025
表示，编码块的第三图像分量临时值用
Figure PCTCN2018107109-appb-000026
表示，则子预设模型中的第一子模型参数α *和第二子模型参数β *如下：
Figure PCTCN2018107109-appb-000027
在得到第一子模型参数α *和第二子模型参数β *之后，可以建立子预设模型，所建立的子预设模型如式(9)所示，
Figure PCTCN2018107109-appb-000028
这样，对于位置坐标为[i,j]的像素点，根据已经获得的第二图像分量重建值
Figure PCTCN2018107109-appb-000029
结合如上式(9)所述的子预设模型，可以得到位置坐标为[i,j]像素点对应的第三图像分量预测值
Figure PCTCN2018107109-appb-000030
可以理解地，上述应用于CCLM的预测模式的预测方法，也适用于MMLM的预测模式；顾名思义，MMLM的预测模式是有多个预设模型来实现由第一图像分量预测第二图像分量；因此，基于图8所示的技术方案，在一种可能的实现方式中，所述方法还包括：
根据所述编码块至少一个像素点对应的第一图像分量重建值，获得至少一个阈值；
根据所述第一图像分量重建值与所述至少一个阈值的比较结果进行分组，得到至少两组第一图像分量重建值和第二图像分量临时值；
根据所述至少两组第一图像分量重建值和第二图像分量临时值中的每一组分别进行模型参数的确定，获取至少两组模型参数。
在上述实现方式中，具体地，所述根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值，包括：
基于所述获取的至少两组模型参数，建立至少两个预设模型；其中，所述至少两个预设模型与所述至少两组模型参数具有对应关系；
根据所述第一图像分量重建值与所述至少一个阈值的比较结果，从所述至少两个预设模型中选取所述编码块中每个像素点对应的预设模型；
根据所述编码块中每个像素点对应的预设模型以及所述第一图像分量重建值，获取所述编码块中每个像素点对应的第二图像分量预测值。
需要说明的是，阈值是编码块的第一图像分量重建值的分类依据。其中，阈值是用于指示建立多个预设模型所依据的设定值，阈值的大小与编码块对应的第一图像分量重建值有关；具体地，可以通过计算编码块对应的第一图像分量重建值的均值得到，也可以通过计算编码块对应的第一图像分量重建值的中值得到，本申请实施例对此不作具体限定。
在本申请实施例中，假定根据所述编码块至少一个像素点对应的第一图像分量重建值以及式(10)，计算得出均值Mean：
Figure PCTCN2018107109-appb-000031
其中，Mean表示所述编码块对应的第一图像分量重建值的均值，
Figure PCTCN2018107109-appb-000032
表示所述编码块对应的第一图像分量重建值之和，M表示所述编码块对应的第一图像分量重建值采样个数。
在计算得出均值Mean之后，可以将该均值Mean直接作为阈值，利用该阈值可以建立两个预设模型；但是需要说明的是，本申请实施例并不限于只建立两个预设模型。这里，本实施例中将编码块对应的第一图像分量重建值的均值Mean作为阈值，即阈值Threshold＝Mean，将编码块对应的第一图像分量重建值与Threshold进行比较，可以得到两组第一图像分量重建值和第二图像分量临时值；根据所述两组第一图像分量重建值和第二图像分量临时值，可以分别推导得出第一预设模型的第一模型参数α 1'和第二模型参数β 1'以及第二预设模型的第一模型参数α 2'和第二模型参数β 2'；结合式(11)，建立第一预设模型M1'和第二预设模型M2'：
Figure PCTCN2018107109-appb-000033
在得到第一预设模型M1'和第二预设模型M2'之后，将编码块中每个像素点的第一图像分量重建值
Figure PCTCN2018107109-appb-000034
与Threshold进行比较，若
Figure PCTCN2018107109-appb-000035
则选择第一预设模型M1'，根据第一预设模型来获取编码块位置坐标为[i,j]像素点对应的第二图像分量预测值Pred 1C[i,j]；若
Figure PCTCN2018107109-appb-000036
则选择第二预设模型M2'，根据第二预设模型来获取编码块位置坐标为[i,j]像素点对应的第二图像分量预测值Pred 2C[i,j]。
在本申请实施例中，在根据所述编码块对应的第一图像分量重建值进行计算得到均值Mean之后，通过将第一图像分量重建值与均值Mean进行比较，以Mean为分界点，若第一图像分量重建值小于等于阈值，则划分到第一组；若第一图像分量重建值大于阈值，则划分到第二组；这样可以得到第一组的第一图像分量重建值集合和第二组的第一图像分量重建值集合。为了简化预设模型的建立，比如根据“两点确定一线”原则，此时还可以针对第一组的第一图像分量重建值集合继续进行中值计算，获取得到第一组的中值(需要说明的是，第一组中仅剩一个像素点对应的重建值)；再针对第二组的第一图像分量重建值集合继续进行中值计算，获取得到第二组的中值(需要说明的是，第二组中也仅剩一个像素点对应的重建值)；根据第一组中仅剩一个像素点对应的值和第二组中仅剩一个像素点对应的值，可以得到这两个像素点分别对应的第一图像分量重建值和第二图像分量临时值，从而可以确定出模型参数，根据模型参数来建立预设模型，也就可以根据所建立的预设模型得到第二图像分量预测值；这种预测方法也可以大大减小第二图像分量预测的计算量。
本实施例提供了一种视频图像分量的预测方法，通过获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值；在本申请实施例中，模型参数的确定不仅考虑了第一图像分量相邻参考值和第二图像分量相邻参考值，而且还考虑了第一图像分量重建值，可以使得第二图像分量预测值更加接近于第二图像分量原始值，从而能够有效提高视频图像分量的预测准确度，使得视频图像分量预测值更加接近于视频图像分量原始值，进而节省了编码码率。
基于前述实施例相同的发明构思，参见图9，其示出了本申请实施例提供的一种视频图像分量的预测装置90的组成，所述视频图像分量的预测装置90可以包括：获取部分901、确定部分902和预测部分903；其中，
所述获取部分901，配置为获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；
所述确定部分902，配置为根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；
所述预测部分903，配置为根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值。
在上述方案中，所述获取部分901，还配置为根据所述第一图像分量重建值、所述 第一图像分量相邻参考值和所述第二图像分量相邻参考值，获取第二图像分量临时值；其中，所述第二图像分量临时值表征所述编码块至少一个像素点对应第二图像分量的临时值；
所述确定部分902，配置为根据所述第一图像分量重建值和所述获取的第二图像分量临时值，确定模型参数。
在上述方案中，参见图10，所述视频图像分量的预测装置90还包括计算部分904，其中，
所述计算部分904，配置为针对所述编码块每个像素点，将所述第一图像分量相邻参考值中任一个参考值与所述每个像素点对应的第一图像分量重建值进行差值计算；
所述获取部分901，还配置为根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的匹配像素点；以及将所述匹配像素点对应的第二图像分量相邻参考值，作为每个像素点对应的第二图像分量临时值。
在上述方案中，所述获取部分901，配置为根据所述差值计算的结果，获取差值最小的第一图像分量相邻参考值所对应的相邻像素点；以及将所述相邻像素点作为每个像素点的匹配像素点。
在上述方案中，所述获取部分901，配置为根据所述差值计算的结果，获取差值最小的第一图像分量相邻参考值所对应的相邻像素点集合；
所述计算部分904，还配置为计算所述相邻像素点集合中每个相邻像素点与所述每个像素点之间的距离值；
所述获取部分901，还配置为选取所述距离值最小的相邻像素点作为每个像素点的匹配像素点。
在上述方案中，所述计算部分904，配置为针对所述编码块每个像素点，将所述第一图像分量相邻参考值中任一个参考值与所述每个像素点对应的第一图像分量重建值进行差值计算；
所述获取部分901，还配置为根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的第一匹配像素点和第二匹配像素点；其中，所述第一匹配像素点表示所述第一图像分量相邻参考值中大于所述第一图像分量重建值且所述差值最小的第一图像分量相邻参考值对应的相邻像素点，所述第二匹配像素点表示所述第一图像分量相邻参考值中小于所述第一图像分量重建值且所述差值最小的第一图像分量相邻参考值对应的相邻像素点；以及根据所述第一匹配像素点对应的第二图像分量相邻参考值和所述第二匹配像素点对应的第二图像分量相邻参考值并进行插值运算，得到每个像素点对应的第二图像分量临时值。
在上述方案中，所述模型参数包括第一模型参数和第二模型参数，所述获取部分901，还配置为将所述第一图像分量重建值和所述第二图像分量临时值输入至第一预设因子计算模型中，获得所述第一模型参数；
所述获取部分901，还配置为将所述第一模型参数、所述第一图像分量重建值和所述第二图像分量临时值输入至第二预设因子计算模型中，获得所述第二模型参数。
在上述方案中，在上述方案中，参见图11，所述视频图像分量的预测装置90还包括建立部分905，其中，
所述建立部分905，配置为基于所述第一模型参数和所述第二模型参数，建立预设模型；其中，所述预设模型用于表征所述编码块中每个像素点对应的第一图像分量重建值与第二图像分量预测值之间的计算关系；
所述预测部分903，还配置为根据所述预设模型和所述编码块中每个像素点对应的第一图像分量重建值，得到所述编码块中每个像素点对应的第二图像分量预测值。
在上述方案中，所述获取部分901，还配置为获取所述编码块对应的第二图像分量重建值和第三图像分量相邻参考值；其中，所述第二图像分量重建值表征所述编码块至少一个像素点对应的第二图像分量重建值，所述第三图像分量相邻参考值表征所述编码块相邻参考像素中每个相邻像素点对应的第三图像分量参考值；
所述确定部分902，还配置为根据获取的所述第二图像分量重建值、所述第二图像分量相邻参考值和所述第三图像分量相邻参考值，确定子模型参数；
所述预测部分903，还配置为根据所述子模型参数，获取所述编码块中每个像素点对应的第三图像分量预测值。
在上述方案中，所述获取部分901，配置为根据所述第二图像分量重建值、所述第二图像分量相邻参考值和所述第三图像分量相邻参考值，获取第三图像分量临时值；其中，所述第三图像分量临时值是基于所述编码块至少一个像素点在所述编码块相邻参考像素中的匹配像素点所对应的第三图像分量相邻参考值得到的；
所述确定部分902，配置为根据所述第二图像分量重建值和所述获取的第三图像分量临时值，确定子模型参数。
在上述方案中，所述获取部分901，还配置为根据所述编码块至少一个像素点对应的第一图像分量重建值，获得至少一个阈值；以及根据所述第一图像分量重建值与所述至少一个阈值的比较结果进行分组，得到至少两组第一图像分量重建值和第二图像分量临时值；以及根据所述至少两组第一图像分量重建值和第二图像分量临时值中的每一组分别进行模型参数的确定，获取至少两组模型参数。
在上述方案中，所述建立部分905，还配置为基于所述获取的至少两组模型参数，建立至少两个预设模型；其中，所述至少两个预设模型与所述至少两组模型参数具有对应关系；
所述预测部分903，还配置为根据所述第一图像分量重建值与所述至少一个阈值的比较结果，从所述至少两个预设模型中选取所述编码块中每个像素点对应的预设模型；以及根据所述编码块中每个像素点对应的预设模型以及所述第一图像分量重建值，获取所述编码块中每个像素点对应的第二图像分量预测值。
可以理解地，在本实施例中，“部分”可以是部分电路、部分处理器、部分程序或软件等等，当然也可以是单元，还可以是模块也可以是非模块化的。
另外，在本实施例中的各组成部分可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。
所述集成的单元如果以软件功能模块的形式实现并非作为独立的产品进行销售或使用时，可以存储在一个计算机可读取存储介质中，基于这样的理解，本实施例的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或processor(处理器)执行本实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
因此，本实施例提供了一种计算机存储介质，该计算机存储介质存储有视频图像分量的预测程序，所述视频图像分量的预测程序被至少一个处理器执行时实现上述图8所示的技术方案中所述的方法的步骤。
基于上述视频图像分量的预测装置90的组成以及计算机存储介质，参见图12，其示出了本申请实施例提供的视频图像分量的预测装置90的具体硬件结构，可以包括： 网络接口1201、存储器1202和处理器1203；各个组件通过总线系统1204耦合在一起。可理解，总线系统1204用于实现这些组件之间的连接通信。总线系统1204除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。但是为了清楚说明起见，在图12中将各种总线都标为总线系统1204。其中，网络接口1201，用于在与其他外部网元之间进行收发信息过程中，信号的接收和发送；
存储器1202，用于存储能够在处理器1203上运行的计算机程序；
处理器1203，用于在运行所述计算机程序时，执行：
获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；
根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；
根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值。
可以理解，本申请实施例中的存储器1202可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDRSDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(Synchlink DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DRRAM)。本文描述的系统和方法的存储器1202旨在包括但不限于这些和任意其它适合类型的存储器。
而处理器1203可能是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法的各步骤可以通过处理器1203中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器1203可以是通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本申请实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器1202，处理器1203读取存储器1202中的信息，结合其硬件完成上述方法的步骤。
可以理解的是，本文描述的这些实施例可以用硬件、软件、固件、中间件、微码或其组合来实现。对于硬件实现，处理单元可以实现在一个或多个专用集成电路(Application Specific Integrated Circuits，ASIC)、数字信号处理器(Digital Signal Processing，DSP)、数字信号处理设备(DSP Device，DSPD)、可编程逻辑设备 (Programmable Logic Device，PLD)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)、通用处理器、控制器、微控制器、微处理器、用于执行本申请所述功能的其它电子单元或其组合中。
对于软件实现，可通过执行本文所述功能的模块(例如过程、函数等)来实现本文所述的技术。软件代码可存储在存储器中并通过处理器执行。存储器可以在处理器中或在处理器外部实现。
可选地，作为另一个实施例，处理器1203还配置为在运行所述计算机程序时，执行上述图8所示的技术方案中所述视频图像分量的预测方法的步骤。
需要说明的是：本申请实施例所记载的技术方案之间，在不冲突的情况下，可以任意组合。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。
工业实用性
本申请实施例中，获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值；从而能够有效提高视频图像分量的预测准确度，使得视频图像分量预测值更加接近于视频图像分量原始值，进而节省了编码码率。

Claims (15)

  1. 一种视频图像分量的预测方法，所述方法包括：
    获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；
    根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；
    根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值。
  2. 根据权利要求1所述的方法，其中，所述根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数，包括：
    根据所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，获取第二图像分量临时值；其中，所述第二图像分量临时值表征所述编码块至少一个像素点对应第二图像分量的临时值；
    根据所述第一图像分量重建值和所述获取的第二图像分量临时值，确定模型参数。
  3. 根据权利要求2所述的方法，其中，所述根据所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，获取第二图像分量临时值，包括：
    针对所述编码块每个像素点，将所述第一图像分量相邻参考值中任一个参考值与所述每个像素点对应的第一图像分量重建值进行差值计算；
    根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的匹配像素点；
    将所述匹配像素点对应的第二图像分量相邻参考值，作为每个像素点对应的第二图像分量临时值。
  4. 根据权利要求3所述的方法，其中，所述根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的匹配像素点，包括：
    根据所述差值计算的结果，获取差值最小的第一图像分量相邻参考值所对应的相邻像素点；
    将所述相邻像素点作为每个像素点的匹配像素点。
  5. 根据权利要求3所述的方法，其中，所述根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的匹配像素点，包括：
    根据所述差值计算的结果，获取差值最小的第一图像分量相邻参考值所对应的相邻像素点集合；
    计算所述相邻像素点集合中每个相邻像素点与所述每个像素点之间的距离值，并且选取所述距离值最小的相邻像素点作为每个像素点的匹配像素点。
  6. 根据权利要求2所述的方法，其中，所述根据所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，获取第二图像分量临时值，包括：
    针对所述编码块每个像素点，将所述第一图像分量相邻参考值中任一个参考值与所述每个像素点对应的第一图像分量重建值进行差值计算；
    根据所述差值计算的结果，从所述编码块相邻参考像素中获取每个像素点的第一匹 配像素点和第二匹配像素点；其中，所述第一匹配像素点表示所述第一图像分量相邻参考值中大于所述第一图像分量重建值且所述差值最小的第一图像分量相邻参考值对应的相邻像素点，所述第二匹配像素点表示所述第一图像分量相邻参考值中小于所述第一图像分量重建值且所述差值最小的第一图像分量相邻参考值对应的相邻像素点；
    根据所述第一匹配像素点对应的第二图像分量相邻参考值和所述第二匹配像素点对应的第二图像分量相邻参考值并进行插值运算，得到每个像素点对应的第二图像分量临时值。
  7. 根据权利要求2所述的方法，其中，所述模型参数包括第一模型参数和第二模型参数，所述根据获取的所述第一图像分量重建值和所述获取的第二图像分量临时值，确定模型参数，包括：
    将所述第一图像分量重建值和所述第二图像分量临时值输入至第一预设因子计算模型中，获得所述第一模型参数；
    将所述第一模型参数、所述第一图像分量重建值和所述第二图像分量临时值输入至第二预设因子计算模型中，获得所述第二模型参数。
  8. 根据权利要求7所述的方法，其中，所述根据所述模型参数，获取所述编码块中每个像素点对应的第二图像分量预测值，包括：
    基于所述第一模型参数和所述第二模型参数，建立预设模型；其中，所述预设模型用于表征所述编码块中每个像素点对应的第一图像分量重建值与第二图像分量预测值之间的计算关系；
    根据所述预设模型和所述编码块中每个像素点对应的第一图像分量重建值，得到所述编码块中每个像素点对应的第二图像分量预测值。
  9. 根据权利要求1至8任一项所述的方法，其中，所述方法还包括：
    获取所述编码块对应的第二图像分量重建值和第三图像分量相邻参考值；其中，所述第二图像分量重建值表征所述编码块至少一个像素点对应的第二图像分量重建值，所述第三图像分量相邻参考值表征所述编码块相邻参考像素中每个相邻像素点对应的第三图像分量参考值；
    根据获取的所述第二图像分量重建值、所述第二图像分量相邻参考值和所述第三图像分量相邻参考值，确定子模型参数；
    根据所述子模型参数，获取所述编码块中每个像素点对应的第三图像分量预测值。
  10. 根据权利要求9所述的方法，其中，所述根据获取的所述第二图像分量重建值、所述第二图像分量相邻参考值和所述第三图像分量相邻参考值，确定子模型参数，包括：
    根据所述第二图像分量重建值、所述第二图像分量相邻参考值和所述第三图像分量相邻参考值，获取第三图像分量临时值；其中，所述第三图像分量临时值是基于所述编码块至少一个像素点在所述编码块相邻参考像素中的匹配像素点所对应的第三图像分量相邻参考值得到的；
    根据所述第二图像分量重建值和所述获取的第三图像分量临时值，确定子模型参数。
  11. 根据权利要求1至10任一项所述的方法，其中，所述方法还包括：
    根据所述编码块至少一个像素点对应的第一图像分量重建值，获得至少一个阈值；
    根据所述第一图像分量重建值与所述至少一个阈值的比较结果进行分组，得到至少两组第一图像分量重建值和第二图像分量临时值；
    根据所述至少两组第一图像分量重建值和第二图像分量临时值中的每一组分别进行模型参数的确定，获取至少两组模型参数。
  12. 根据权利要求11所述的方法，其中，所述根据所述模型参数，获取所述编码 块中每个像素点对应的第二图像分量预测值，包括：
    基于所述获取的至少两组模型参数，建立至少两个预设模型；其中，所述至少两个预设模型与所述至少两组模型参数具有对应关系；
    根据所述第一图像分量重建值与所述至少一个阈值的比较结果，从所述至少两个预设模型中选取所述编码块中每个像素点对应的预设模型；
    根据所述编码块中每个像素点对应的预设模型以及所述第一图像分量重建值，获取所述编码块中每个像素点对应的第二图像分量预测值。
  13. 一种视频图像分量的预测装置，所述视频图像分量的预测装置包括：获取部分、确定部分和预测部分；
    所述获取部分，配置为获取编码块对应的第一图像分量重建值、第一图像分量相邻参考值和第二图像分量相邻参考值；其中，所述第一图像分量重建值表征所述编码块至少一个像素点对应第一图像分量的重建值，所述第一图像分量相邻参考值和所述第二图像分量相邻参考值分别表征所述编码块相邻参考像素中每个相邻像素点对应第一图像分量的参考值和第二图像分量的参考值；
    所述确定部分，配置为根据获取的所述第一图像分量重建值、所述第一图像分量相邻参考值和所述第二图像分量相邻参考值，确定模型参数；
    所述预测部分，配置为根据所述模型参数，获取所述编码块每个像素点对应的第二图像分量预测值。
  14. 一种视频图像分量的预测装置，其中，所述视频图像分量的预测装置包括：存储器和处理器；
    所述存储器，用于存储能够在所述处理器上运行的计算机程序；
    所述处理器，用于在运行所述计算机程序时，执行权利要求1至12任一项所述的方法的步骤。
  15. 一种计算机存储介质，其中，所述计算机存储介质存储有视频图像分量的预测程序，所述视频图像分量的预测程序被至少一个处理器执行时实现权利要求1至12任一项所述的方法的步骤。
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