WO2024022390A1 - Procédé et appareil d'amélioration des performances d'un modèle inter-composantes convolutif dans un système de codage vidéo - Google Patents

Procédé et appareil d'amélioration des performances d'un modèle inter-composantes convolutif dans un système de codage vidéo Download PDF

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WO2024022390A1
WO2024022390A1 PCT/CN2023/109349 CN2023109349W WO2024022390A1 WO 2024022390 A1 WO2024022390 A1 WO 2024022390A1 CN 2023109349 W CN2023109349 W CN 2023109349W WO 2024022390 A1 WO2024022390 A1 WO 2024022390A1
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convolutional
cross
target
filter
block
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PCT/CN2023/109349
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Cheng-Yen Chuang
Ching-Yeh Chen
Chih-Wei Hsu
Tzu-Der Chuang
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Mediatek Inc.
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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
    • H04N19/11—Selection of coding mode or of prediction mode among a plurality of spatial predictive coding modes
    • 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
    • H04N19/105—Selection of the reference unit for prediction within a chosen coding or prediction mode, e.g. adaptive choice of position and number of pixels used for prediction
    • 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
    • H04N19/119—Adaptive subdivision aspects, e.g. subdivision of a picture into rectangular or non-rectangular coding blocks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/186—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/80—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation
    • H04N19/82—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation involving filtering within a prediction loop

Definitions

  • the present invention is a non-Provisional Application of and claims priority to U.S. Provisional Patent Application No. 63/369,524, filed on July 27, 2022.
  • the U.S. Provisional Patent Application is hereby incorporated by reference in its entirety.
  • the present invention relates to video coding system.
  • the present invention relates to schemes to improve performance or reducing the complexity of CCLM (Convolutional Cross-Component Model) related modes in a video coding system.
  • CCLM Convolutional Cross-Component Model
  • VVC Versatile video coding
  • JVET Joint Video Experts Team
  • MPEG ISO/IEC Moving Picture Experts Group
  • ISO/IEC 23090-3 2021
  • Information technology -Coded representation of immersive media -Part 3 Versatile video coding, published Feb. 2021.
  • VVC is developed based on its predecessor HEVC (High Efficiency Video Coding) by adding more coding tools to improve coding efficiency and also to handle various types of video sources including 3-dimensional (3D) video signals.
  • HEVC High Efficiency Video Coding
  • Fig. 1A illustrates an exemplary adaptive Inter/Intra video coding system incorporating loop processing.
  • Intra Prediction the prediction data is derived based on previously coded video data in the current picture.
  • Motion Estimation (ME) is performed at the encoder side and Motion Compensation (MC) is performed based of the result of ME to provide prediction data derived from other picture (s) and motion data.
  • Switch 114 selects Intra Prediction 110 or Inter-Prediction 112 and the selected prediction data is supplied to Adder 116 to form prediction errors, also called residues.
  • the prediction error is then processed by Transform (T) 118 followed by Quantization (Q) 120.
  • T Transform
  • Q Quantization
  • the transformed and quantized residues are then coded by Entropy Encoder 122 to be included in a video bitstream corresponding to the compressed video data.
  • the bitstream associated with the transform coefficients is then packed with side information such as motion and coding modes associated with Intra prediction and Inter prediction, and other information such as parameters associated with loop filters applied to underlying image area.
  • the side information associated with Intra Prediction 110, Inter prediction 112 and in-loop filter 130, are provided to Entropy Encoder 122 as shown in Fig. 1A. When an Inter-prediction mode is used, a reference picture or pictures have to be reconstructed at the encoder end as well.
  • the transformed and quantized residues are processed by Inverse Quantization (IQ) 124 and Inverse Transformation (IT) 126 to recover the residues.
  • the residues are then added back to prediction data 136 at Reconstruction (REC) 128 to reconstruct video data.
  • the reconstructed video data may be stored in Reference Picture Buffer 134 and used for prediction of other frames.
  • incoming video data undergoes a series of processing in the encoding system.
  • the reconstructed video data from REC 128 may be subject to various impairments due to a series of processing.
  • in-loop filter 130 is often applied to the reconstructed video data before the reconstructed video data are stored in the Reference Picture Buffer 134 in order to improve video quality.
  • deblocking filter (DF) may be used.
  • SAO Sample Adaptive Offset
  • ALF Adaptive Loop Filter
  • the loop filter information may need to be incorporated in the bitstream so that a decoder can properly recover the required information. Therefore, loop filter information is also provided to Entropy Encoder 122 for incorporation into the bitstream.
  • DF deblocking filter
  • SAO Sample Adaptive Offset
  • ALF Adaptive Loop Filter
  • Loop filter 130 is applied to the reconstructed video before the reconstructed samples are stored in the reference picture buffer 134.
  • the system in Fig. 1A is intended to illustrate an exemplary structure of a typical video encoder. It may correspond to the High Efficiency Video Coding (HEVC) system, VP8, VP9, H. 264 or VVC.
  • HEVC High Efficiency Video Coding
  • the decoder can use similar or portion of the same functional blocks as the encoder except for Transform 118 and Quantization 120 since the decoder only needs Inverse Quantization 124 and Inverse Transform 126.
  • the decoder uses an Entropy Decoder 140 to decode the video bitstream into quantized transform coefficients and needed coding information (e.g. ILPF information, Intra prediction information and Inter prediction information) .
  • the Intra prediction 150 at the decoder side does not need to perform the mode search. Instead, the decoder only needs to generate Intra prediction according to Intra prediction information received from the Entropy Decoder 140.
  • the decoder only needs to perform motion compensation (MC 152) according to Inter prediction information received from the Entropy Decoder 140 without the need for motion estimation.
  • an input picture is partitioned into non-overlapped square block regions referred as CTUs (Coding Tree Units) , similar to HEVC.
  • CTUs Coding Tree Units
  • Each CTU can be partitioned into one or multiple smaller size coding units (CUs) .
  • the resulting CU partitions can be in square or rectangular shapes.
  • VVC divides a CTU into prediction units (PUs) as a unit to apply prediction process, such as Inter prediction, Intra prediction, etc.
  • a CTU is split into CUs by using a quaternary-tree (QT) structure denoted as coding tree to adapt to various local characteristics.
  • QT quaternary-tree
  • the decision whether to code a picture area using inter-picture (temporal) or intra-picture (spatial) prediction is made at the leaf CU level.
  • Each leaf CU can be further split into one, two or four Pus according to the PU splitting type. Inside one PU, the same prediction process is applied and the relevant information is transmitted to the decoder on a PU basis.
  • a leaf CU After obtaining the residual block by applying the prediction process based on the PU splitting type, a leaf CU can be partitioned into transform units (TUs) according to another quaternary-tree structure similar to the coding tree for the CU.
  • transform units TUs
  • One of key feature of the HEVC structure is that it has the multiple partition conceptions including CU, PU, and TU.
  • a quadtree with nested multi-type tree using binary and ternary splits segmentation structure replaces the concepts of multiple partition unit types, i.e. it removes the separation of the CU, PU and TU concepts except as needed for CUs that have a size too large for the maximum transform length, and supports more flexibility for CU partition shapes.
  • a CU can have either a square or rectangular shape.
  • a coding tree unit (CTU) is first partitioned by a quaternary tree (a. k. a. quadtree) structure. Then the quaternary tree leaf nodes can be further partitioned by a multi-type tree structure. As shown in Fig.
  • the multi-type tree leaf nodes are called coding units (CUs) , and unless the CU is too large for the maximum transform length, this segmentation is used for prediction and transform processing without any further partitioning. This means that, in most cases, the CU, PU and TU have the same block size in the quadtree with nested multi-type tree coding block structure. The exception occurs when maximum supported transform length is smaller than the width or height of the colour component of the CU.
  • Fig. 3 illustrates the signalling mechanism of the partition splitting information in quadtree with nested multi-type tree coding tree structure.
  • a coding tree unit (CTU) is treated as the root of a quaternary tree and is first partitioned by a quaternary tree structure.
  • Each quaternary tree leaf node (when sufficiently large to allow it) is then further partitioned by a multi-type tree structure.
  • a first flag is signalled to indicate whether the node is further partitioned.
  • a second flag is signalled to indicate whether it's a QT partitioning or MTT partitioning mode.
  • a third flag mtt_split_cu_vertical_flag
  • mtt_split_cu_binary_flag is signalled to indicate whether the split is a binary split or a ternary split.
  • the multi-type tree slitting mode (MttSplitMode) of a CU is derived as shown in Table 1.
  • Fig. 4 shows a CTU divided into multiple CUs with a quadtree and nested multi-type tree coding block structure, where the bold block edges represent quadtree partitioning and the remaining edges represent multi-type tree partitioning.
  • the quadtree with nested multi-type tree partition provides a content-adaptive coding tree structure comprised of CUs.
  • the size of the CU may be as large as the CTU or as small as 4 ⁇ 4 in units of luma samples.
  • the maximum chroma CB size is 64 ⁇ 64 and the minimum size chroma CB consist of 16 chroma samples.
  • the maximum supported luma transform size is 64 ⁇ 64 and the maximum supported chroma transform size is 32 ⁇ 32.
  • the width or height of the CB is larger the maximum transform width or height, the CB is automatically split in the horizontal and/or vertical direction to meet the transform size restriction in that direction.
  • the following parameters are defined for the quadtree with nested multi-type tree coding tree scheme. These parameters are specified by SPS syntax elements and can be further refined by picture header syntax elements.
  • CTU size the root node size of a quaternary tree
  • MinQTSize the minimum allowed quaternary tree leaf node size
  • MaxBtSize the maximum allowed binary tree root node size
  • MaxTtSize the maximum allowed ternary tree root node size
  • MaxMttDepth the maximum allowed hierarchy depth of multi-type tree splitting from a quadtree leaf
  • MinCbSize the minimum allowed coding block node size
  • the CTU size is set as 128 ⁇ 128 luma samples with two corresponding 64 ⁇ 64 blocks of 4: 2: 0 chroma samples
  • the MinQTSize is set as 16 ⁇ 16
  • the MaxBtSize is set as 128 ⁇ 128
  • MaxTtSize is set as 64 ⁇ 64
  • the MinCbsize (for both width and height) is set as 4 ⁇ 4
  • the MaxMttDepth is set as 4.
  • the quaternary tree leaf nodes may have a size from 16 ⁇ 16 (i.e., the MinQTSize) to 128 ⁇ 128 (i.e., the CTU size) . If the leaf QT node is 128 ⁇ 128, it will not be further split by the binary tree since the size exceeds the MaxBtSize and MaxTtSize (i.e., 64 ⁇ 64) . Otherwise, the leaf qdtree node could be further partitioned by the multi-type tree. Therefore, the quaternary tree leaf node is also the root node for the multi-type tree and it has multi-type tree depth (mttDepth) as 0.
  • mttDepth multi-type tree depth
  • the coding tree scheme supports the ability for the luma and chroma to have a separate block tree structure.
  • the luma and chroma CTBs in one CTU have to share the same coding tree structure.
  • the luma and chroma can have separate block tree structures.
  • luma CTB is partitioned into CUs by one coding tree structure
  • the chroma CTBs are partitioned into chroma CUs by another coding tree structure.
  • a CU in an I slice may consist of a coding block of the luma component or coding blocks of two chroma components, and a CU in a P or B slice always consists of coding blocks of all three colour components unless the video is monochrome.
  • VPDUs Virtual Pipeline Data Units
  • Virtual pipeline data units are defined as non-overlapping units in a picture.
  • successive VPDUs are processed by multiple pipeline stages at the same time.
  • the VPDU size is roughly proportional to the buffer size in most pipeline stages, so it is important to keep the VPDU size small.
  • the VPDU size can be set to maximum transform block (TB) size.
  • TB maximum transform block
  • TT ternary tree
  • BT binary tree
  • TT split is not allowed (as indicated by “X” in Fig. 5) for a CU with either width or height, or both width and height equal to 128.
  • processing throughput drops when a picture has smaller intra blocks because of sample processing data dependency between neighbouring intra blocks.
  • the predictor generation of an intra block requires top and left boundary reconstructed samples from neighbouring blocks. Therefore, intra prediction has to be sequentially processed block by block.
  • the smallest intra CU is 8x8 luma samples.
  • the luma component of the smallest intra CU can be further split into four 4x4 luma intra prediction units (PUs) , but the chroma components of the smallest intra CU cannot be further split. Therefore, the worst case hardware processing throughput occurs when 4x4 chroma intra blocks or 4x4 luma intra blocks are processed.
  • chroma intra CBs smaller than 16 chroma samples (size 2x2, 4x2, and 2x4) and chroma intra CBs with width smaller than 4 chroma samples (size 2xN) are disallowed by constraining the partitioning of chroma intra CBs.
  • a smallest chroma intra prediction unit is defined as a coding tree node whose chroma block size is larger than or equal to 16 chroma samples and has at least one child luma block smaller than 64 luma samples, or a coding tree node whose chroma block size is not 2xN and has at least one child luma block 4xN luma samples. It is required that in each SCIPU, all CBs are inter, or all CBs are non-inter, i.e., either intra or intra block copy (IBC) .
  • IBC intra block copy
  • chroma of the non-inter SCIPU shall not be further split and luma of the SCIPU is allowed to be further split.
  • the small chroma intra CBs with size less than 16 chroma samples or with size 2xN are removed.
  • chroma scaling is not applied in case of a non-inter SCIPU.
  • no additional syntax is signalled, and whether a SCIPU is non-inter can be derived by the prediction mode of the first luma CB in the SCIPU.
  • the type of a SCIPU is inferred to be non-inter if the current slice is an I-slice or the current SCIPU has a 4x4 luma partition in it after further split one time (because no inter 4x4 is allowed in VVC) ; otherwise, the type of the SCIPU (inter or non-inter) is indicated by one flag before parsing the CUs in the SCIPU.
  • the 2xN intra chroma blocks are removed by disabling vertical binary and vertical ternary splits for 4xN and 8xN chroma partitions, respectively.
  • the small chroma blocks with sizes 2x2, 4x2, and 2x4 are also removed by partitioning restrictions.
  • a restriction on picture size is considered to avoid 2x2/2x4/4x2/2xN intra chroma blocks at the corner of pictures by considering the picture width and height to be multiple of max (8, MinCbSizeY) .
  • the number of directional intra modes in VVC is extended from 33, as used in HEVC, to 65.
  • the new directional modes not in HEVC are depicted as red dotted arrows in Fig. 6, and the planar and DC modes remain the same.
  • These denser directional intra prediction modes apply for all block sizes and for both luma and chroma intra predictions.
  • every intra-coded block has a square shape and the length of each of its side is a power of 2. Thus, no division operations are required to generate an intra-predictor using DC mode.
  • blocks can have a rectangular shape that necessitates the use of a division operation per block in the general case. To avoid division operations for DC prediction, only the longer side is used to compute the average for non-square blocks.
  • MPM most probable mode
  • a unified 6-MPM list is used for intra blocks irrespective of whether MRL and ISP coding tools are applied or not.
  • the MPM list is constructed based on intra modes of the left and above neighbouring block. Suppose the mode of the left is denoted as Left and the mode of the above block is denoted as Above, the unified MPM list is constructed as follows:
  • Max –Min is equal to 1:
  • Max –Min is greater than or equal to 62:
  • Max –Min is equal to 2:
  • the first bin of the MPM index codeword is CABAC context coded. In total three contexts are used, corresponding to whether the current intra block is MRL enabled, ISP enabled, or a normal intra block.
  • TBC Truncated Binary Code
  • Conventional angular intra prediction directions are defined from 45 degrees to -135 degrees in clockwise direction.
  • VVC several conventional angular intra prediction modes are adaptively replaced with wide-angle intra prediction modes for non-square blocks.
  • the replaced modes are signalled using the original mode indexes, which are remapped to the indexes of wide angular modes after parsing.
  • the total number of intra prediction modes is unchanged, i.e., 67, and the intra mode coding method is unchanged.
  • the top reference with length 2W+1, and the left reference with length 2H+1 are defined as shown in Fig. 7A and Fig. 7B respectively.
  • the Dia. mode in Fig. 7A and Fig. 7B are diagonal modes, i.e., mode 34.
  • the number of replaced modes in wide-angular direction mode depends on the aspect ratio of a block.
  • the replaced intra prediction modes are illustrated in Table 2.
  • Chroma derived mode (DM) derivation table for 4: 2: 2 chroma format was initially ported from HEVC extending the number of entries from 35 to 67 to align with the extension of intra prediction modes. Since HEVC specification does not support prediction angle below -135° and above 45°, luma intra prediction modes ranging from 2 to 5 are mapped to 2. Therefore, chroma DM derivation table for 4: 2: 2: chroma format is updated by replacing some values of the entries of the mapping table to convert prediction angle more precisely for chroma blocks.
  • pred C (i, j) represents the predicted chroma samples in a CU and rec L (i, j) represents the downsampled reconstructed luma samples of the same CU.
  • the CCLM parameters ( ⁇ and ⁇ ) are derived with at most four neighbouring chroma samples and their corresponding down-sampled luma samples. Suppose the current chroma block dimensions are W ⁇ H, then W’ and H’ are set as
  • the four neighbouring luma samples at the selected positions are down-sampled and compared four times to find two larger values: x 0 A and x 1 A , and two smaller values: x 0 B and x 1 B .
  • Their corresponding chroma sample values are denoted as y 0 A , y 1 A , y 0 B and y 1 B .
  • Fig. 8 shows an example of the location of the left and above samples and the sample of the current block involved in the LM_LA mode.
  • Fig. 8 shows the relative sample locations of N ⁇ N chroma block 810, the corresponding 2N ⁇ 2N luma block 820 and their neighbouring samples (shown as filled circles) .
  • the division operation to calculate parameter ⁇ is implemented with a look-up table.
  • the diff value difference between maximum and minimum values
  • LM_A 2 LM modes
  • LM_L 2 LM modes
  • LM_A mode only the above template is used to calculate the linear model coefficients. To get more samples, the above template is extended to (W+H) samples. In LM_L mode, only left template are used to calculate the linear model coefficients. To get more samples, the left template is extended to (H+W) samples.
  • LM_LA mode left and above templates are used to calculate the linear model coefficients.
  • two types of down-sampling filter are applied to luma samples to achieve 2 to 1 down-sampling ratio in both horizontal and vertical directions.
  • the selection of down-sampling filter is specified by a SPS level flag.
  • the two down-sampling filters are as follows, which are corresponding to “type-0” and “type-2” content, respectively.
  • Rec L ′ (i, j) [rec L (2i-1, 2j-1) +2 ⁇ rec L (2i-1, 2j-1) +rec L (2i+1, 2j-1) + rec L (2i-1, 2j) +2 ⁇ rec L (2i, 2j) +rec L (2i+1, 2j) +4] >>3 (6)
  • Rec L ′ (i, j) rec L (2i, 2j-1) +rec L (2i-1, 2j) +4 ⁇ rec L (2i, 2j) +rec L (2i+1, 2j) + rec L (2i, 2j+1) +4] >>3 (7)
  • This parameter computation is performed as part of the decoding process, and is not just as an encoder search operation. As a result, no syntax is used to convey the ⁇ and ⁇ values to the decoder.
  • Chroma mode coding For chroma intra mode coding, a total of 8 intra modes are allowed for chroma intra mode coding. Those modes include five traditional intra modes and three cross-component linear model modes (LM_LA, LM_A, and LM_L) . Chroma mode signalling and derivation process are shown in Table 3 Chroma mode coding directly depends on the intra prediction mode of the corresponding luma block. Since separate block partitioning structure for luma and chroma components is enabled in I slices, one chroma block may correspond to multiple luma blocks. Therefore, for Chroma DM mode, the intra prediction mode of the corresponding luma block covering the centre position of the current chroma block is directly inherited.
  • the first bin indicates whether it is regular (0) or CCLM modes (1) . If it is LM mode, then the next bin indicates whether it is LM_LA (0) or not. If it is not LM_LA, next 1 bin indicates whether it is LM_L (0) or LM_A (1) .
  • the first bin of the binarization table for the corresponding intra_chroma_pred_mode can be discarded prior to the entropy coding. Or, in other words, the first bin is inferred to be 0 and hence not coded.
  • This single binarization table is used for both sps_cclm_enabled_flag equal to 0 and 1 cases.
  • the first two bins in Table 4 are context coded with its own context model, and the rest bins are bypass coded.
  • the chroma CUs in 32x32 /32x16 chroma coding tree node are allowed to use CCLM in the following way:
  • all chroma CUs in the 32x32 node can use CCLM
  • all chroma CUs in the 32x16 chroma node can use CCLM.
  • CCLM is not allowed for chroma CU.
  • MMLM Multiple Model CCLM
  • MMLM multiple model CCLM mode
  • JEM J. Chen, E. Alshina, G.J. Sullivan, J. -R. Ohm, and J. Boyce, Algorithm Description of Joint Exploration Test Model 7, document JVET-G1001, ITU-T/ISO/IEC Joint Video Exploration Team (JVET) , Jul. 2017
  • MMLM multiple model CCLM mode
  • neighbouring luma samples and neighbouring chroma samples of the current block are classified into two groups, each group is used as a training set to derive a linear model (i.e., a particular ⁇ and ⁇ are derived for a particular group) .
  • the samples of the current luma block are also classified based on the same rule for the classification of neighbouring luma samples.
  • LIC Local illumination compensation
  • LIC Local Illumination Compensation
  • LIC is a method of inter prediction by using neighbouring samples of current block and reference block. It is based on a linear model using a scaling factor a and an offset b. It derives the scaling factor a and the offset b by referring to the neighbouring samples of current block and reference block.
  • the coding tool is enabled or disabled adaptively for each CU.
  • JVET-C1001 J. Chen, et al., “Algorithm Description of Joint Exploration Test Model 3” , Joint Video Exploration Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11, 3rd Meeting: Geneva, CH, 26 May –1 June 2016, document JVET-C1001) .
  • CCCM Convolutional cross-component model
  • a convolutional model is applied to improve the chroma prediction performance.
  • the convolutional model uses a 7-tap filter consisting of a 5-tap plus sign shape spatial component, a nonlinear term and a bias term.
  • the input to the spatial 5-tap components of the filter consist of a centre (C) luma sample which is collocated with the chroma sample to be predicted and its above/north (N) , below/south (S) , left/west (W) and right/east (E) neighbours as shown in Fig. 10.
  • the bias term (denoted as B) represents a scalar offset between the input and output (similarly to the offset term in CCLM) and is set to middle chroma value (e.g. 512 for 10-bit contents) .
  • the filter coefficients c i are calculated by minimising MSE between predicted and reconstructed chroma samples in the reference area.
  • Fig. 11 illustrates the reference area which consists of 6 lines of chroma samples above and left of the PU. Reference area extends one PU width to the right and one PU height below the PU boundaries. Area is adjusted to include only available samples. The extensions to the area shown in grey are needed to support the “side samples” of the plus shaped spatial filter and are padded if unavailable.
  • the MSE minimization is performed by calculating autocorrelation matrix for the luma input and a cross-correlation vector between the luma input and chroma output.
  • Autocorrelation matrix is LDL decomposed and the final filter coefficients are calculated using back-substitution.
  • LDL decomposition was chosen instead of Cholesky decomposition to avoid using square root operations.
  • the Cholesky decomposition or Cholesky factorization is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions.
  • the Cholesky decomposition is very efficient for solving systems of linear equations.
  • the LDL decomposition is a closely related variant of the classical Cholesky decomposition.
  • CCCM Convolutional Cross-Component Model
  • a method and apparatus of convolutional cross-component prediction model (CCCM) for video coding are disclosed.
  • input data associated with a current block comprising a luma block and a chroma block are received, wherein the input data comprise pixel data to be encoded at an encoder side or coded data associated with the current block to be decoded at a decoder side.
  • An auto-correlation matrix for reference luma samples in a reference area is derived, wherein the auto-correlation matrix is associated with a footprint of a convolutional filter.
  • a cross-correlation vector between the reference luma samples and reference chroma samples in the reference area is derived, wherein the cross-correlation vector is associated with the footprint of the convolutional filter.
  • Coefficients of the convolutional filter are derived based on the auto-correlation matrix and the cross-correlation vector using Gaussian elimination scheme.
  • a convolutional cross-component model predictor for the target chroma sample is generated by applying the convolutional filter with the coefficients derived to a corresponding location of the luma block.
  • a final predictor is generated for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor.
  • the target chroma sample is encoded or decoded using the final predictor.
  • a number of filter taps for a target convolutional filter is determined depending on one or more conditions.
  • a convolutional cross-component model predictor for the target chroma sample by applying the target convolutional filter to a corresponding location of the luma block.
  • a final predictor is generated for the target chroma sample comprising the convolutional cross-component model predictor.
  • the target chroma sample is encoded or decoded using the final predictor.
  • said one or more conditions correspond to one or more pre-defined implicit rules.
  • the target convolutional filter is determined implicitly according to a current block size.
  • the target convolutional filter is derived by setting a derived coefficient of the reference convolutional filter to zero if the derived coefficient is smaller than a pre-defined threshold value. In another embodiment, the target convolutional filter is derived by setting a derived coefficient of the reference convolutional filter to zero if the derived coefficient is smaller than a sum of coefficients of the reference convolutional filter multiplied by a pre-defined threshold value.
  • the target convolutional filter is selected from multiple convolutional filters with different numbers of filter taps.
  • the target convolutional filter is selected from multiple convolutional filters generated by separating the reference convolutional filter. In one embodiment, one of said multiple convolutional filters achieving a best performance is explicitly signalled as the target convolutional filter.
  • the target convolutional filter is selected from multiple convolutional filters with different shapes according to a block-level syntax.
  • the target convolutional filter comprises an optional non-linear tap and a syntax is used to indicate whether the optional non-linear tap is used.
  • one or more syntax elements to indicate a target convolutional cross-component filter selected from multiple convolutional cross-component filters are signalled or parsed, wherein each convolutional cross-component filter uses at least two different luma samples from two different positions.
  • a convolutional cross-component model predictor for the target chroma sample is generated by applying the target convolutional cross-component filter to a corresponding location of the luma block.
  • a final predictor is generated for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor.
  • the target chroma sample is encoded or decoded using the final predictor.
  • said one or more syntax elements are signalled or parsed at an SPS (Sequence Parameter Set) , PPS (Picture Parameter Set) , PH (Picture Header) , SH (Slice Header) , or CTU (Coding Tree Unit) level.
  • SPS Sequence Parameter Set
  • PPS Picture Parameter Set
  • PH Physical Header
  • SH Slice Header
  • CTU Coding Tree Unit
  • Fig. 1A illustrates an exemplary adaptive Inter/Intra video coding system incorporating loop processing.
  • Fig. 1B illustrates a corresponding decoder for the encoder in Fig. 1A.
  • Fig. 2 illustrates examples of a multi-type tree structure corresponding to vertical binary splitting (SPLIT_BT_VER) , horizontal binary splitting (SPLIT_BT_HOR) , vertical ternary splitting (SPLIT_TT_VER) , and horizontal ternary splitting (SPLIT_TT_HOR) .
  • Fig. 3 illustrates an example of the signalling mechanism of the partition splitting information in quadtree with nested multi-type tree coding tree structure.
  • Fig. 4 shows an example of a CTU divided into multiple CUs with a quadtree and nested multi-type tree coding block structure, where the bold block edges represent quadtree partitioning and the remaining edges represent multi-type tree partitioning.
  • Fig. 5 shows some examples of TT split forbidden when either width or height of a luma coding block is larger than 64.
  • Fig. 6 shows the intra prediction modes as adopted by the VVC video coding standard.
  • Figs. 7A-B illustrate examples of wide-angle intra prediction a block with width larger than height (Fig. 7A) and a block with height larger than width (Fig. 7B) .
  • Fig. 8 shows an example of the location of the left and above samples and the sample of the current block involved in the LM_LA mode.
  • Fig. 9 shows an example of classifying the neighbouring samples into two groups according to multiple mode CCLM.
  • Fig. 10 illustrates an example of spatial part of the convolutional filter for CCCM.
  • Fig. 11 illustrates an example of reference area (with its paddings) used to derive the CCCM filter coefficients.
  • Fig. 12 illustrates the 3x2 down-sampling filter used for down-sampling the luma samples for YUV420 colour format.
  • Figs. 13A-B illustrate two kinds of filter shapes with numbers of filter taps according to one embodiment of the present invention.
  • Figs. 14A-D illustrate four kinds of filter shapes with numbers of filter taps according to one embodiment of the present invention.
  • Figs. 15A-D illustrate four kinds of filter shapes with numbers of filter taps according to one embodiment of the present invention.
  • Fig. 16 illustrates a flowchart of an exemplary video coding system that incorporates a CCCM (Convolutional Cross-Component Model) related mode using Gaussian elimination according to an embodiment of the present invention.
  • CCCM Convolutional Cross-Component Model
  • Fig. 17 illustrates a flowchart of an exemplary video coding system that utilises multiple CCCM models with numbers of filter taps according to an embodiment of the present invention.
  • Fig. 18 illustrates a flowchart of an exemplary video coding system that incorporates signalling for selecting a target filter from multiple CCCM models according to an embodiment of the present invention.
  • Gaussian elimination method can be used to derive the optimal coefficients of CCCM model.
  • Gaussian elimination also known as row reduction, is an algorithm for solving systems of linear equations. It consists of a sequence of operations performed on the corresponding matrix of coefficients. To perform row reduction on a matrix, one uses a sequence of elementary row operations to modify the matrix until the lower left-hand corner of the matrix is filled with zeros, as much as possible.
  • the Gaussian elimination method has lower computational complexity than LDL decomposition and Cholesky decomposition.
  • the LDL or Cholesky decomposition as used by the conventional CCCM for MSE minimization is replaced by the Gaussian elimination to reduce the computational complexity.
  • the autocorrelation matrix for the luma input and a cross-correlation vector between the luma input and chroma output are calculated first.
  • the autocorrelation matrix for the luma input can be calculated according to the footprint of the reference convolutional filter (e.g. the 7-tap CCCM filter) using luma data in the reference area (e.g. the reference area in Fig. 11) .
  • the cross-correlation vector between the luma input and chroma output can be calculated according to the footprint of the reference convolutional filter (e.g. the 7-tap CCCM filter) using luma data and chroma data in the reference area (e.g. the reference area in Fig. 11) .
  • CCCM uses a 7-tap filter to formulate a chroma prediction model, which has been shown to achieve noticeable coding gain.
  • solving the optimal parameters for a 7-tap filter can be quite complicated, especially on tiny chroma blocks. Overfitting on the reference samples can also be a problem.
  • several methods are proposed to reduce the number of filter taps from an initial or a reference convolutional filter (e.g. the 7-tap filter used in the conventional CCCM) implicitly or explicitly.
  • CCCM model with adaptive filter shape is proposed to reduce the number of filter taps. It will adaptively reduce the number of filter taps by one or more pre-defined implicit rules.
  • the implicit selection of CCCM model with adaptive filter shapes is dependent on the CU size. In one embodiment, one or more predefined values are used for the filter taps instead of deriving it at the decoder side.
  • a derived coefficient of CCCM model is smaller than a pre-defined threshold value, the coefficient will be set it to zero to reduce the number of filter taps.
  • the sum of all derived coefficients of the reference convolutional filter is calculated first. If one coefficient is smaller than the sum of all coefficients of the reference convolutional filter multiplied by a pre-defined threshold value, we will set this coefficient to zero to reduce the number of filter taps.
  • CCCM models with different numbers of filter taps.
  • the model with more filter taps performs much better than the model with less filter taps, the model with more filter taps will be selected. Otherwise, we would rather select the model with less filter taps, even if it is slightly worse than the model with more filter taps.
  • Fig. 13A centre, left, right, non-linear and bias terms
  • Fig. 13B centre, above, below, non-linear and bias terms
  • predChromaVal 1 c 0 C + c 1 E + c 2 W + c 3 P + c 4 B
  • predChromaVal 2 c 0 C + c 1 N + c 2 S + c 3 P + c 4 B.
  • the first one consists of centre, right, non-linear and bias terms (Fig. 14A) ; the second one consists of centre, left, non-linear and bias terms (Fig. 14B) ; the third one consists of centre, above, non-linear and bias terms (Fig. 14C) ; the fourth one consists of centre, below, non-linear and bias terms (Fig. 14D) .
  • predChromaVal 1 c 0 C + c 1 E + c 2 P + c 3 B
  • predChromaVal 2 c 0 C + c 1 W + c 2 P + c 3 B
  • predChromaVal 3 c 0 C + c 1 N + c 2 P + c 3 B
  • predChromaVal 4 c 0 C + c 1 S + c 2 P + c 3 B.
  • Fig. 15A centre, right, above, non-linear and bias terms
  • Fig. 15B centre, left, above, non-linear and bias terms
  • Fig. 15C centre, right, below, non-linear and bias terms
  • Fig. 15D centre, left, below, non-linear and bias terms
  • predChromaVal 1 c 0 C + c 1 E + c 2 N + c 3 P + c 4 B
  • predChromaVal 2 c 0 C + c 1 W + c 2 N + c 3 P + c 4 B
  • predChromaVal 3 c 0 C + c 1 W + c 2 S + c 3 P + c 4 B
  • predChromaVal 4 c 0 C + c 1 E + c 2 S + c 3 P + c 4 B.
  • the non-linear term is removed to further reduce the model complexity.
  • the non-linear term is optional and decided by the encoder. Additional flag is signalled to enable the non-linear term.
  • allowing a CCCM model with multiple filter shapes can depend on a high-level syntax.
  • a flag can be signalled in the SPS (Sequence Parameter Set) , PPS (Picture Parameter Set) , PH (Picture Header) , SH (Slice Header) , or CTU (Coding Tree Unit) level.
  • allowing a CCCM model with multiple filter shapes can depend on a block-level syntax. For example, a flag can be signalled for each CU, or be signalled for each chroma block.
  • indicating which filter shape is used in CCCM can depend on a syntax, and the syntax can be coded by truncated unary coding or truncated binary coding.
  • the method of using a differential form to build the CCCM model is proposed. All or part of adjacent spatial terms are replaced by differential terms, which are the differences between respective adjacent spatial terms and the central term.
  • a clipping operation can be applied to the differential terms to reduce the noise.
  • a 6-tap CCCM model which consists of four spatial differential terms, one non-linear term and one bias term.
  • the differential terms are the difference between respective adjacent sample values and the central sample value.
  • a 6-tap CCCM model which consists of four spatial differential terms, one non-linear term and one bias term.
  • the differential terms correspond to the clipped differences between respectively adjacent sample values and the central sample value, where the clipping operation is performed after computing the difference.
  • clipped differential term is used in CCCM model.
  • the pre-defined clipping threshold value of each differential term can be different or partially different.
  • clipped differential term is used in CCCM model.
  • the first one use differential terms, and the second one use clipped differential terms.
  • a flag can be signalled for indicating the selected model.
  • the CCCM Convolutional Cross-Component Model
  • CCCM Convolutional Cross-Component Model
  • any of the proposed CCCM methods can be implemented in an Intra coding module (e.g. Intra pred. 150 in Fig. 1B) and/or an inter prediction module (e.g. MC 152 in Fig. 1B) in a decoder or an Intra coding module (e.g. Intra Pred. 110 in Fig. 1A) and/or an inter prediction module (e.g. Inter Pred. 112 in Fig. 1A) in an encoder.
  • Intra coding module e.g. Intra pred. 150 in Fig. 1B
  • an inter prediction module e.g. MC 152 in Fig. 1B
  • an Intra coding module e.g. Intra Pred. 110 in Fig. 1A
  • an inter prediction module e.g. Inter Pred. 112 in Fig. 1A
  • any of the proposed CCCM methods can also be implemented as a circuit coupled to the intra/inter coding module at the decoder or the encoder.
  • the decoder or encoder may also use additional processing unit to implement the required CCCM processing.
  • the Intra Pred. units e.g. unit 110 in Fig. 1A and unit 150 in Fig. 1B
  • inter prediction module e.g. Inter Pred. 112 in Fig. 1Aand MC 152 in Fig. 1B
  • DSP Digital Signal Processor
  • FPGA Field Programmable Gate Array
  • Fig. 16 illustrates a flowchart of an exemplary video coding system that incorporates a CCCM (Convolutional Cross-Component Model) related mode using Gaussian elimination according to an embodiment of the present invention.
  • the steps shown in the flowchart may be implemented as program codes executable on one or more processors (e.g., one or more CPUs) at the encoder side.
  • the steps shown in the flowchart may also be implemented based hardware such as one or more electronic devices or processors arranged to perform the steps in the flowchart.
  • input data associated with a current block comprising a luma block and a chroma block are received in step 1610, wherein the input data comprise pixel data to be encoded at an encoder side or coded data associated with the current block to be decoded at a decoder side.
  • An auto-correlation matrix for reference luma samples in a reference area is derived in step 1620, wherein the auto-correlation matrix is associated with a footprint of a convolutional filter.
  • a cross-correlation vector between the reference luma samples and reference chroma samples in the reference area is derived in step 1630, wherein the cross-correlation vector is associated with the footprint of the convolutional filter.
  • Coefficients of the convolutional filter are derived based on the auto-correlation matrix and the cross-correlation vector using Gaussian elimination scheme in step 1640.
  • a convolutional cross-component model predictor for the target chroma sample is generated by applying the convolutional filter with the coefficients derived to a corresponding location of the luma block in step 1650.
  • a final predictor is generated for the target chroma sample from a set of prediction candidates comprising the convolutional cross-component model predictor in step 1660.
  • the target chroma sample is encoded or decoded using the final predictor in step 1670.
  • Fig. 17 illustrates a flowchart of an exemplary video coding system that utilises multiple CCCM models with numbers of filter taps according to an embodiment of the present invention.
  • input data associated with a current block comprising a luma block and a chroma block are received in step 1710, wherein the input data comprise pixel data to be encoded at an encoder side or coded data associated with the current block to be decoded at a decoder side.
  • a number of filter taps for target convolutional filter is determined depending on one or more conditions in step 1720.
  • a convolutional cross-component model predictor for the target chroma sample is generated by applying the target convolutional filter to a corresponding location of the luma block in step 1730.
  • a final predictor is generated for the target chroma sample is generated comprising the convolutional cross-component model predictor in step 1740.
  • the target chroma sample is encoded or decoded using the final predictor in step 1750.
  • Fig. 18 illustrates a flowchart of an exemplary video coding system that incorporates signalling for selecting a target filter from multiple CCCM models according to an embodiment of the present invention.
  • input data associated with a current block comprising a luma block and a chroma block are received in step 1810, wherein the input data comprise pixel data to be encoded at an encoder side or coded data associated with the current block to be decoded at a decoder side.
  • One or more syntax elements to indicate a target convolutional cross-component filter selected from multiple convolutional cross-component filters are signalled or parsed in step 1820, wherein each convolutional cross-component filter uses at least two different luma samples from two different positions.
  • a convolutional cross-component model predictor for the target chroma sample is generated by applying the target convolutional filter to a corresponding location of the luma block in step 1830.
  • a final predictor is generated for the target chroma sample is generated comprising the convolutional cross-component model predictor in step 1840.
  • the target chroma sample is encoded or decoded using the final predictor in step 1850.
  • Embodiment of the present invention as described above may be implemented in various hardware, software codes, or a combination of both.
  • an embodiment of the present invention can be one or more circuit circuits integrated into a video compression chip or program code integrated into video compression software to perform the processing described herein.
  • An embodiment of the present invention may also be program code to be executed on a Digital Signal Processor (DSP) to perform the processing described herein.
  • DSP Digital Signal Processor
  • the invention may also involve a number of functions to be performed by a computer processor, a digital signal processor, a microprocessor, or field programmable gate array (FPGA) .
  • These processors can be configured to perform particular tasks according to the invention, by executing machine-readable software code or firmware code that defines the particular methods embodied by the invention.
  • the software code or firmware code may be developed in different programming languages and different formats or styles.
  • the software code may also be compiled for different target platforms.
  • different code formats, styles and languages of software codes and other means of configuring code to perform the tasks in accordance with the invention will not depart from the spirit and scope of the invention.

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Abstract

L'invention concerne des procédés et un appareil de CCCM pour le codage vidéo. Selon un procédé, une matrice d'auto-corrélation associée à une empreinte d'un filtre convolutif pour des échantillons de luminance de référence dans une zone de référence est dérivée. Un vecteur de corrélation croisée, associé à l'empreinte, entre les échantillons de luminance de référence et des échantillons de chrominance de référence dans la zone de référence est dérivé. Des coefficients du filtre convolutif sont dérivés sur la base de la matrice d'auto-corrélation et du vecteur de corrélation croisée au moyen d'un schéma d'élimination gaussienne. Pour un échantillon de chrominance cible dans le bloc de chrominance, le filtre convolutif avec les coefficients dérivés est appliqué à un emplacement correspondant du bloc de luminance afin de former un prédicteur de modèle inter-composantes convolutif pour l'échantillon de chrominance cible. Un prédicteur final est généré pour l'échantillon de chrominance cible à partir d'un ensemble de candidats de prédiction comprenant le prédicteur de modèle inter-composantes convolutif. L'échantillon de chrominance cible est codé ou décodé au moyen du prédicteur final.
PCT/CN2023/109349 2022-07-27 2023-07-26 Procédé et appareil d'amélioration des performances d'un modèle inter-composantes convolutif dans un système de codage vidéo WO2024022390A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109842799A (zh) * 2017-11-29 2019-06-04 杭州海康威视数字技术股份有限公司 颜色分量的帧内预测方法及装置
WO2019162117A1 (fr) * 2018-02-23 2019-08-29 Canon Kabushiki Kaisha Procédés et dispositifs pour une amélioration dans l'obtention de paramètres de prédiction d'échantillons de composantes linéaires
WO2019162116A1 (fr) * 2018-02-23 2019-08-29 Canon Kabushiki Kaisha Nouveaux ensembles d'échantillons et nouveaux schémas de sous-échantillonnage pour une prédiction d'échantillon de composante linéaire
CN114270826A (zh) * 2019-08-06 2022-04-01 现代自动车株式会社 用于视频数据的帧内预测编码的方法和装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109842799A (zh) * 2017-11-29 2019-06-04 杭州海康威视数字技术股份有限公司 颜色分量的帧内预测方法及装置
WO2019162117A1 (fr) * 2018-02-23 2019-08-29 Canon Kabushiki Kaisha Procédés et dispositifs pour une amélioration dans l'obtention de paramètres de prédiction d'échantillons de composantes linéaires
WO2019162116A1 (fr) * 2018-02-23 2019-08-29 Canon Kabushiki Kaisha Nouveaux ensembles d'échantillons et nouveaux schémas de sous-échantillonnage pour une prédiction d'échantillon de composante linéaire
CN114270826A (zh) * 2019-08-06 2022-04-01 现代自动车株式会社 用于视频数据的帧内预测编码的方法和装置

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