METHOD AND APPARATUS OF ALF COMPLEXITY REDUCTION FOR CROSS-COMPONENT TAPS IN VIDEO CODING
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CROSS REFERENCE TO RELATED APPLICATIONS
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The present invention is a non-Provisional Application of and claims priority to U.S. Provisional Patent Application No. 63/510, 152, filed on June 26, 2023. The U.S. Provisional Patent Application is hereby incorporated by reference in its entirety.
FIELD OF THE INVENTION
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The present invention relates to video coding system using ALF (Adaptive Loop Filter) . In particular, the present invention relates to simplifying the ALF filtering process to reduce the complexity.
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BACKGROUND AND RELATED ART
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Versatile video coding (VVC) is the latest international video coding standard developed by the Joint Video Experts Team (JVET) of the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG) . The standard has been published as an ISO standard: 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.
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Fig. 1A illustrates an exemplary adaptive Inter/Intra video encoding system incorporating loop processing. For Intra Prediction, the prediction data is derived based on previously coded video data in the current picture. For Inter Prediction 112, Motion Estimation (ME) is performed at the encoder side and Motion Compensation (MC) is performed based on 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. 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. Consequently, 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.
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As shown in Fig. 1A, 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. Accordingly, 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.
For example, deblocking filter (DF) , Sample Adaptive Offset (SAO) and Adaptive Loop Filter (ALF) may be used. 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. In Fig. 1A, 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.
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The decoder, as shown in Fig. 1B, 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. Instead of Entropy Encoder 122, 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. Furthermore, for Inter prediction, 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.
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According to VVC, an input picture is partitioned into non-overlapped square block regions referred as CTUs (Coding Tree Units) , similar to HEVC. 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. Also, VVC divides a CTU into prediction units (PUs) as a unit to apply prediction process, such as Inter prediction, Intra prediction, etc.
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Adaptive Loop Filter in VVC
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In VVC, an Adaptive Loop Filter (ALF) with block-based filter adaption is applied. For the luma component, one filter is selected among 25 filters for each 4×4 block, based on the direction and activity of local gradients.
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1. Filter shape
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Two diamond filter shapes (as shown in Fig. 2) are used. The 7×7 diamond shape 220 is applied for luma component and the 5×5 diamond shape 210 is applied for chroma components.
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2. Block classification
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For luma component, each 4×4 block is categorized into one out of 25 classes. The classification index C is derived based on its directionality D and a quantized value of activityas follows:
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To calculate D andgradients of the horizontal, vertical and two diagonal direction are first calculated using 1-D Laplacian:
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where indices i and j refer to the coordinates of the upper left sample within the 4×4 block and R (i, j) indicates a reconstructed sample at coordinate (i, j) .
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To reduce the complexity of block classification, the subsampled 1-D Laplacian calculation is applied to the vertical direction (Fig. 3A) and the horizontal direction (Fig. 3B) . As shown in Figs. 3C-D, the same subsampled positions are used for gradient calculation of all directions (gd1 in Fig. 3C and gd2 in Fig. 3D) .
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Then D maximum and minimum values of the gradients of horizontal and vertical directions are set as:
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The maximum and minimum values of the gradient of two diagonal directions are set as:
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To derive the value of the directionality D, these values are compared against each other and with two thresholds t1 and t2:
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Step 1. If bothandare true, D is set to 0.
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Step 2. Ifcontinue from Step 3; otherwise continue from Step 4.
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Step 3. IfD is set to 2; otherwise D is set to 1.
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Step 4. IfD is set to 4; otherwise D is set to 3.
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The activity value A is calculated as:
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A is further quantized to the range of 0 to 4, inclusively, and the quantized value is denoted as
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For chroma components in a picture, no classification is applied.
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3. Geometric transformations of filter coefficients and clipping values
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Before filtering each 4×4 luma block, geometric transformations such as rotation or diagonal and vertical flipping are applied to the filter coefficients f (k, l) and to the corresponding filter clipping values c (k, l) depending on gradient values calculated for that block. This is equivalent to applying these transformations to the samples in the filter support region. The idea is to make different blocks to which ALF is applied more similar by aligning their directionality.
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Three geometric transformations, including diagonal, vertical flip and rotation are introduced:
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Diagonal: fD (k, l) =f (l, k) , cD (k, l) =c (l, k) ,
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Vertical flip: fV (k, l) =f (k, K-l-1) , cV (k, l) =c (k, K-l-1) ,
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Rotation: fR (k, l) =f (K-l-1, k) , cR (k, l) =c (K-l-1, k) ,
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where K is the size of the filter and 0≤k, l≤K-1 are coefficients coordinates, such that location (0, 0) is at the upper left corner and location (K-1, K-1) is at the lower right corner. The transformations are applied to the filter coefficients f (k, l) and to the clipping values c (k, l) depending on gradient values calculated for that block. The relationship between the transformation and the four gradients of the four directions are summarized in the following table.
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Table 1. Mapping of the gradient calculated for one block and the transformations
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4. Filtering process
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At decoder side, when ALF is enabled for a CTB, each sample R (i, j) within the CU is filtered, resulting in sample value R′ (i, j) as shown below,
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where f (k, l) denotes the decoded filter coefficients, K (x, y) is the clipping function and c (k, l) denotes the decoded clipping parameters. The variable k and l varies between –L/2 and L/2, where L denotes the filter length. The clipping function K (x, y) =min (y, max (-y, x) ) which corresponds to the function Clip3 (-y, y, x) . The clipping operation introduces non-linearity to make ALF more efficient by reducing the impact of neighbour sample values that are too different with the current sample value.
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5. Cross component adaptive loop filter
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CC-ALF uses luma sample values to refine each chroma component by applying an adaptive, linear filter to the luma channel and then using the output of this filtering operation for chroma refinement. Fig. 4A provides a system level diagram of the CC-ALF process with respect to the SAO, luma ALF and chroma ALF processes. As shown in Fig. 4A, each colour component (i.e., Y, Cb and Cr) is processed by its respective SAO (i.e., SAO Luma 410, SAO Cb 412 and SAO Cr 414) . After SAO, ALF Luma 420 is applied to the SAO-processed luma and ALF Chroma 430 is applied to SAO-processed Cb and Cr. However, there is a cross-component term from luma to a chroma component (i.e., CC-ALF Cb 422 and CC-ALF Cr 424) . The outputs from the cross-component ALF are added (using adders 432 and 434 respectively) to the outputs from ALF Chroma 430.
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Filtering in CC-ALF is accomplished by applying a linear, diamond shaped filter (e.g. filters 440 and 442 in Fig. 4B) to the luma channel. In Fig. 4B, a blank circle indicates a luma sample and a dot-filled circle indicate a chroma sample. One filter is used for each chroma channel, and the operation is expressed as:
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where (x, y) is chroma component i location being refined, (xY, yY) is the luma location based on (x, y) , Si is filter support area in luma component, and ci (x0, y0) represents the filter coefficients.
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As shown in Fig, 4B, the luma filter support is the region collocated with the current chroma sample after accounting for the spatial scaling factor between the luma and chroma planes.
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In the VVC reference software, CC-ALF filter coefficients are computed by minimizing the mean square error of each chroma channel with respect to the original chroma content. To achieve this, the VTM (VVC Test Model) algorithm uses a coefficient derivation process similar to the one used for chroma ALF. Specifically, a correlation matrix is derived, and the coefficients are computed using a Cholesky decomposition solver in an attempt to minimize a mean square error metric. In designing the filters, a maximum of 8 CC-ALF filters can be designed and transmitted per picture. The resulting filters are then
indicated for each of the two chroma channels on a CTU basis.
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Additional characteristics of CC-ALF include:
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· The design uses a 3x4 diamond shape with 8 taps.
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· Seven filter coefficients are transmitted in the APS.
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· Each of the transmitted coefficients has a 6-bit dynamic range and is restricted to power-of-2 values.
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· The eighth filter coefficient is derived at the decoder such that the sum of the filter coefficients is equal to 0.
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· An APS may be referenced in the slice header.
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· CC-ALF filter selection is controlled at CTU-level for each chroma component
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· Boundary padding for the horizontal virtual boundaries uses the same memory access pattern as luma ALF.
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As an additional feature, the reference encoder can be configured to enable some basic subjective tuning through the configuration file. When enabled, the VTM attenuates the application of CC-ALF in regions that are coded with high QP and are either near mid-grey or contain a large amount of luma high frequencies. Algorithmically, this is accomplished by disabling the application of CC-ALF in CTUs where any of the following conditions are true:
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· The slice QP value minus 1 is less than or equal to the base QP value.
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· The number of chroma samples for which the local contrast is greater than (1 << (bitDepth –2 ) ) –1 exceeds the CTU height, where the local contrast is the difference between the maximum and minimum luma sample values within the filter support region.
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· More than a quarter of chroma samples are in the range between (1 << (bitDepth –1 ) ) –16 and (1 << (bitDepth –1 ) ) + 16
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The motivation for this functionality is to provide some assurance that CC-ALF does not amplify artefacts introduced earlier in the decoding path (This is largely due the fact that the VTM currently does not explicitly optimize for chroma subjective quality) . It is anticipated that alternative encoder implementations may either not use this functionality or incorporate alternative strategies suitable for their encoding characteristics.
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6. Filter parameters signalling
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ALF filter parameters are signalled in Adaptation Parameter Set (APS) . In one APS, up to 25 sets of luma filter coefficients and clipping value indexes, and up to eight sets of chroma filter coefficients and clipping value indexes can be signalled. To reduce bits overhead, filter coefficients of different classification for luma component can be merged. In slice header, the indices of the APSs used for the current slice are signalled.
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Clipping value indexes, which are decoded from the APS, allow determining clipping values using a table of clipping values for both luma and Chroma components. These clipping values are dependent of the internal bitdepth. More precisely, the clipping values are obtained by the following formula:
AlfClip= {round (2B-α*n) for n∈ [0.. N-1] }
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with B equal to the internal bitdepth, α is a pre-defined constant value equal to 2.35, and N equal to 4 which is the number of allowed clipping values in VVC. The AlfClip is then rounded to the nearest value with the
format of power of 2.
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In slice header, up to 7 APS indices can be signalled to specify the luma filter sets that are used for the current slice. The filtering process can be further controlled at CTB level. A flag is always signalled to indicate whether ALF is applied to a luma CTB. A luma CTB can choose a filter set among 16 fixed filter sets and the filter sets from APSs. A filter set index is signalled for a luma CTB to indicate which filter set is applied. The 16 fixed filter sets are pre-defined and hard-coded in both the encoder and the decoder.
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For the chroma component, an APS index is signalled in slice header to indicate the chroma filter sets being used for the current slice. At CTB level, a filter index is signalled for each chroma CTB if there is more than one chroma filter set in the APS.
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The filter coefficients are quantized with norm equal to 128. In order to restrict the multiplication complexity, a bitstream conformance is applied so that the coefficient value of the non-central position shall be in the range of -27 to 27 -1, inclusive. The central position coefficient is not signalled in the bitstream and is considered as equal to 128.
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Adaptive Loop Filter in ECM
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In ECM7 (Muhammed Coban, et al., “Algorithm description of Enhanced Compression Model 7 (ECM 7) ” , Joint Video Experts Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29) , 28th Meeting, Mainz, DE, 20–28 October 2022, Document: JVET-AB2025) , some changes from the VVC ALF are disclosed. A brief overview is shown below.
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1. ALF simplification
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ALF gradient subsampling and ALF virtual boundary processing are removed. Block size for classification is reduced from 4x4 to 2x2. Filter size for both luma and chroma, for which ALF coefficients are signalled, is increased to 9x9.
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2. ALF with fixed filters
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To filter a luma sample, three different classifiers (C0, C1 and C2) and three different sets of filters (F0, F1 and F2) are used. Sets F0 and F1 contain fixed filters, with coefficients trained for classifiers C0 and C1. Coefficients of filters in F2 are signalled. Which filter from a set Fi is used for a given sample is decided by a class Ci assigned to this sample using classifier Ci.
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3. Filtering
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At first, two 13x13 diamond shape fixed filters F0 and F1 are applied to derive two intermediate samples R0 (x, y) and R1 (x, y) . After that, F2 is applied to R0 (x, y) , R1 (x, y) , and neighbouring samples to derive a filtered sample as
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where fi, j is the clipped difference between a neighbouring sample and current sample R (x, y) and gi is the clipped difference between Ri-20 (x, y) and current sample. The filter coefficients ci, i=0, …21, are signalled.
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4. Classification
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Based on directionality Di and activityaclass Ci is assigned to each 2x2 block:
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where MD, i represents the total number of directionalities Di.
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As in VVC, values of the horizontal, vertical, and two diagonal gradients are calculated for each sample using 1-D Laplacian. The sum of the sample gradients within a 4×4 window that covers the target
2×2 block is used for classifier C0 and the sum of sample gradients within a 12×12 window is used for classifiers C1 and C2. The sums of horizontal, vertical and two diagonal gradients are denoted, respectively, asandThe directionality Di is determined by comparing
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with a set of thresholds. The directionality D2 is derived as in VVC using thresholds 2 and 4.5. For D0 and D1, horizontal/vertical edge strengthand diagonal edge strengthare calculated first. Thresholds Th=[1.25, 1.5, 2, 3, 4.5, 8] are used. Edge strengthis 0 ifotherwise, is the maximum integer such thatEdge strengthis 0 ifotherwise, is the maximum integer such thatWheni.e., horizontal/vertical edges are dominant, the Di is derived by using Table 2A; otherwise, diagonal edges are dominant, the Di is derived by using Table 2B.
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Table 2A. Mapping ofandto Di
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Table 2B. Mapping ofandto Di
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To obtainthe sum of vertical and horizontal gradients Ai is mapped to the range of 0 to n, where n is equal to 4 forand 15 forand
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In an ALF_APS, up to 4 luma filter sets are signalled, each set may have up to 25 filters.
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5. Alternative 2x2 ALF classifier
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Classification in ALF is extended with an additional alternative classifier. For a signalled luma filter set, a flag is signalled to indicate whether the alternative classifier is applied. Geometrical transformation is not applied to the alternative band classifier. When the band-based classifier is applied, the sum of sample values of a 2x2 luma block is calculated at first. Then the class index is calculated as below, class_index = (sum *25) >> (sample bit depth + 2) .
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6. CCALF with long tap filter
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The CCALF process uses a linear filter to filter luma sample values and generate a residual correction for the chroma samples. A 25-tap large filter is used in CCALF process, which is illustrated in Fig. 5. In Fig. 5, the 25 taps for luma samples are shown in grey dots and the location of the corresponding chroma sample is shown as a small dash-lined circle. For a given slice, the encoder can collect the statistics of the slice, analyze them and can signal up to 16 filters through APS.
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7. Adaptive filter shape switch/using samples before deblocking filter for ALF
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Two candidate filter shapes: a diamond shape as shown in Fig. 6 and a new cross shape as shown in Fig. 7, can be adaptively selected by the luma filters in ALF. The number of coefficients of a luma filter is 22 for both the filter shapes. Please note that these 22 taps are constituted with 20 spatial taps (610 and 710 in Fig. 6 and Fig. 7 respectively) and 2 fixed filters based taps (620 and 720 in Fig. 6 and Fig. 7 respectively) in both shapes.
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In each Adaptation Parameter Set (APS) , a shape index for the derived luma filters is signalled to the decoder. Each APS contains the luma filters that are associated with the filter shape index.
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For each CTB, an APS index is signalled to indicate which luma filter shape is used to filter the current CTB. When filtering a luma sample, the coefficients and clip indices are also rearranged according to the corresponding filter shape.
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The diamond shape luma ALF is replaced by the longer filter shown in Fig. 7.
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The samples before deblocking filters are used as additional inputs for ALF. A final ALF sample is derived by weighting the regular ALF and the filter applied to the samples before the deblocking filter. Specifically, a filtered sample is derived as
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where fi, j is the clipped difference between a neighbouring sample and current sample R (x, y) , gi is the clipped difference between an intermediate sample and current sample R (x, y) and hi, j is the clipped difference between a neighbouring sample before DBF and current sample R (x, y) . The filter coefficients ci, i=0, …24 are signalled. In example, 3x3 diamond shape is applied to samples before deblocking filter. In an APS, a flag is signalled to indicate whether samples before DBF are used for ALF which is always set as true at encoder.
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8. Extended fixed-filter-output based taps for ALF
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In ALF of ECM-7.0 (Fabrice Le Léannec, et al., “Algorithm description of Enhanced Compression Model 7 (ECM 7) ” , Joint Video Experts Team (JVET) of ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29 28th Meeting, Mainz, DE, 20–28 October 2022, Document: JVET-AB2025) , an online-trained filter consists of 3 kinds of filter taps: spatial taps (810) , reconstruction-before-DBF based taps (840) and fixed-filter-output based taps (820 and 830) , as shown in Fig. 8. The spatial taps (i.e., tap #0 ～ #19) located in a cross shape are followed by 3 reconstruction-before-DBF based taps (i.e., tap #20, #21, #24) and 2 additional offline-filtered taps (i.e., tap #22, #23) , resulting in 25 taps in total.
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By adding the extended fixed-filter-output based taps, as shown in Fig. 9, the number of fixed-filter-output based taps (920 and 930) are increased from 2 to 8 (i.e., tap #20 ～ #25 and tap #28 ～ #29) while the spatial taps (910) and the reconstruction-before-DBF based taps (940) are kept unchanged.
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9. ALF with residual samples
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The residual samples are used as additional inputs to the ALF. A filtered sample is derived as:
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where ri is the clipped neighbouring residual sample value and rFilteredi is the clipped residual sample filtered by the fixed-filter. For residual samples, the fixed filter reuses the offline fixed filter trained for reconstruction after SAO.
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The complete filter shape of ALF using residual samples as additional inputs (1010 and 1020) is shown in Fig. 10 and remaining inputs (910-940) are the same as the ALF in Fig. 9.
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In an adaptation parameter set, a flag is signalled to indicate whether only residual based taps or both residual based taps and residual filtered by fixed filter based taps are used for the ALF.
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Convolutional Cross-Component Intra Prediction Model
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In this method convolutional cross-component model (CCCM) is applied to predict chroma samples from reconstructed luma samples in a similar spirit as done by the current CCLM modes. As with CCLM, the reconstructed luma samples are down-sampled to match the lower resolution chroma grid when chroma sub-sampling is used. Similar to CCLM top, left or top and left reference samples are used as templates for model derivation.
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Also, similarly to CCLM, there is an option of using a single model or multi-model variant of CCCM. The multi-model variant uses two models, one model derived for samples above the average luma reference value and another model for the rest of the samples (following the spirit of the CCLM design) . Multi-model CCCM mode can be selected for PUs which have at least 128 reference samples available.
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1. Convolutional filter
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The convolutional 7-tap filter consist of a 5-tap plus sign shape spatial component, a nonlinear term and a bias term. The input to the spatial 5-tap component of the filter consists 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. 11.
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The nonlinear term P is represented as power of two of the centre luma sample C and scaled to the sample value range of the content:
P = (C*C + midVal ) >> bitDepth.
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For 10-bit content it is calculated as:
P = (C*C + 512 ) >> 10.
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The bias term B represents a scalar offset between the input and output (similarly to the offset term in CCLM) and is set to middle chroma value (512 for 10-bit content) .
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Output of the filter is calculated as a convolution between the filter coefficients ci and the input values and clipped to the range of valid chroma samples:
predChromaVal = c0C + c1N + c2S + c3E + c4W + c5P + c6B.
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2. Calculation of filter coefficients
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The filter coefficients ci are calculated by minimising MSE between predicted and reconstructed chroma samples in the reference area. Fig. 12 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 blue are needed to support the “side samples” of the plus shaped spatial filter and are padded
when in unavailable areas.
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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. The process follows roughly the calculation of the ALF filter coefficients in ECM, however LDL decomposition was chosen instead of Cholesky decomposition to avoid using square root operations.
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The autocorrelation matrix is calculated using the reconstructed values of luma and chroma samples. These samples are full range (e.g. between 0 and 1023 for 10-bit content) resulting in relatively large values in the autocorrelation matrix. This requires high bit depth operation during the model parameters calculation. It is proposed to remove fixed offsets from luma and chroma samples in each PU for each model. This is driving down the magnitudes of the values used in the model creation and allows reducing the precision needed for the fixed-point arithmetic. As a result, 16-bit decimal precision is proposed to be used instead of the 22-bit precision of the original CCCM implementation.
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Reference sample values just outside of the top-left corner of the PU are used as the offsets (offsetLuma, offsetCb and offsetCr) for simplicity. The samples values used in both model creation and final prediction (i.e., luma and chroma in the reference area, and luma in the current PU) are reduced by these fixed values, as follows:
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C'= C –offsetLuma
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N'= N –offsetLuma
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S'= S –offsetLuma
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E'= E –offsetLuma
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W'= W –offsetLuma
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P'= nonLinear (C')
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B = midValue = 1 << (bitDepth -1) .
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and the chroma value is predicted using the following equation, where offsetChroma is equal to offsetCr and offsetCb for Cr and Cb components, respectively:
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predChromaVal = c0C'+ c1N'+ c2S'+ c3E'+ c4W'+ c5P'+ c6B + offsetChroma.
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In order to avoid any additional sample level operations, the luma offset is removed during the luma reference sample interpolation. This can be done, for example, by substituting the rounding term used in the luma reference sample interpolation with an updated offset including both the rounding term and the offsetLuma. The chroma offset can be removed by deducting the chroma offset directly from the reference chroma samples. As an alternative way, impact of the chroma offset can be removed from the cross-component vector giving identical result. In order to add the chroma offset back to the output of the convolutional prediction operation the chroma offset is added to the bias term of the convolutional model.
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The process of CCCM model parameter calculation requires division operations. Division operations are not always considered implementation friendly. The division operations are replaced with multiplication (with a scale factor) and shift operation, where a scale factor and number of shifts are calculated based on denominator similar to the method used in calculation of CCLM parameters.
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3. Gradient Linear Model
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For YUV 4: 2: 0 colour format, a Gradient Linear Model (GLM) method can be used to predict the chroma samples from luma sample gradients. Two modes are supported: a two-parameter GLM mode and a three-parameter GLM mode.
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Compared with the CCLM, instead of down-sampled luma values, the two-parameter GLM utilizes luma sample gradients to derive the linear model. Specifically, when the two-parameter GLM is applied, the input to the CCLM process, i.e., the down-sampled luma samples L, are replaced by luma sample gradients G. The other parts of the CCLM (e.g., parameter derivation, prediction sample linear transform) are kept unchanged:
C=α·G+β.
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In the three-parameter GLM, a chroma sample can be predicted based on both the luma sample gradients and down-sampled luma values with different parameters. The model parameters of the three-parameter GLM are derived from 6 rows and columns adjacent samples by the LDL decomposition based MSE minimization method as used in the CCCM:
C=α0·G+α1·L+α2·β.
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For signalling, when the CCLM mode is enabled to the current CU, one flag is signalled to indicate whether GLM is enabled for both Cb and Cr components; if the GLM is enabled, another flag is signalled to indicate which of the two GLM modes is selected and one syntax element is further signalled to select one of 4 gradient filters for the gradient calculation.
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Four gradient filters (1310-1340) are enabled for the GLM, as illustrated in Fig. 13.
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4. CCCM signalling
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Usage of the mode is signalled with a CABAC coded PU level flag. One new CABAC context was included to support this. When it comes to signalling, CCCM is considered a sub-mode of CCLM. That is, the CCCM flag is only signalled if intra prediction mode is LM_CHROMA.
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5. CCCM using non-downsampled luma samples
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CCCM mode with 3x2 filter using non-downsampled luma samples is used, which consists of 6-tap spatial terms, four nonlinear terms and a bias term. The 6-tap spatial terms correspond to 6 neighbouring luma samples (i.e., L0, L1, …, L5) around the chroma sample (i.e., C) to be predicted, the four non-linear terms are derived from the samples L0, L1, L2, and L3 as shown in Fig. 14.
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where αi is the coefficient, β is the offset. Same to the existing CCCM design, up to 6 lines/columns of chroma samples above and left to the current CU are applied to derive the filter coefficients. The filter coefficients are derived based on the same LDL decomposition method used in CCCM. The proposed method is signalled as an additional CCCM model besides the existing one, when the CCCM is selected, one single flag is signalled and used for both two chroma components to indicate whether the default CCCM model or the proposed CCCM model is applied. Additionally, SPS signalling is introduced to indicate whether the CCCM using non-downsampled luma samples is enabled.
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6. Gradient and location based convolutional cross-component model (GL-CCCM)
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This method maps luma values into chroma values using a filter with inputs consisting of one spatial luma sample, two gradient values, two location information, a nonlinear term, and a bias term. The GL-CCCM method uses gradient and location information instead of the 4 spatial neighbour samples used in the CCCM filter. The GL-CCCM filter used for the prediction is:
predChromaVal = c0C + c1Gy + c2Gx + c3Y + c4X + c5P + c6B,
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where Gy and Gx are the vertical and horizontal gradients, respectively, and are calculated as Fig. 15:
Gy = (2N + NW + NE) – (2S + SW + SE)
Gx = (2W + NW + SW) – (2E + NE + SE)
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Moreover, the Y and X are the spatial coordinates of the center luma sample.
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The rest of the parameters are the same as CCCM tool. The reference area for the parameter calculation is the same as CCCM method.
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The usage of the mode is signalled with a CABAC coded PU level flag. When it comes to signalling, GL-CCCM is considered a sub-mode of CCCM. That is, the GL-CCCM flag is only signalled if original CCCM flag is true.
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Similar to the CCCM, GL-CCCM tool has 6 modes for calculating the parameters:
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· Single-model GL-CCCM from above and left templates
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· Single-model GL-CCCM from above template
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· Single-model GL-CCCM from left template
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· Multi-model GL-CCCM from above and left templates
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· Multi-model GL-CCCM from above template
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· Multi-model GL-CCCM from left template
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The encoder performs SATD search for the 6 GL-CCCM modes along with the existing CCCM modes to find the best candidates for full RD tests.
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The reconstructed luma or chroma samples of current block used for deriving samples from cross-component model can be samples before applying ALF filtering process or samples after applying ALF filtering process.
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In the present invention, methods and apparatus to simplify the ALF filtering process are disclosed.
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BRIEF SUMMARY OF THE INVENTION
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A method and apparatus for video coding using ALF (Adaptive Loop Filter) are disclosed. According to the method, input data for a current block comprising a first-colour block and a second-colour block is received, wherein the input data comprises reconstructed first-colour samples of the first-colour block and reconstructed second-colour samples of the second-colour block. Model parameters for cross-component model is derived by using sub-sampled first-colour samples and sub-sampled second-colour samples in a neighbouring area of the current block, the current block, or both. One or more cross-component second-colour samples are derived by applying the cross-component model with the model parameters derived to the reconstructed first-colour samples of the first-colour block. Target ALF processing is applied to the current block to generate a filtered second-colour block, wherein the target ALF processing comprises taps associated with said one or more cross-component second-colour samples. The filtered second-colour block is provided.
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In one embodiment, the first-colour block corresponds to one luma block and the second-colour block corresponds to one chroma block, or the first-colour block corresponds to one chroma block and the second-colour block corresponds to one luma block. In another embodiment, the first-colour block corresponds to a first chroma block and the second-colour block corresponds to a second chroma block.
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In one embodiment, the sub-sampled first-colour samples and the sub-sampled second-colour samples in the neighbouring area of the current block, the current block, or both for deriving the model parameters are before applying deblocking filtering process, after applying the deblocking filtering process,
before applying SAO (Sample Adaptive Offset) filtering process, after applying the SAO filtering process, before applying bilateral filtering process, or after applying the bilateral filtering process. In another embodiment, the sub-sampled first-colour samples and the sub-sampled second-colour samples in the neighbouring area of the current block, the sub-sampled first-colour samples in the current block, or both for deriving the model parameters are before or after applying ALF filtering process.
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In one embodiment, the sub-sampled first-colour samples and the sub-sampled second-colour samples in the neighbouring area of the current block, the current block, or both for deriving the model parameters are reconstructed samples, prediction samples or residual samples.
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In one embodiment, the cross-component model corresponds to CCCM (Convolution Cross-Component Model) .
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In one embodiment, the cross-component model derived for the current block is used by a subsequent block to a right or below the current block. In one embodiment, the model parameters for the cross-component model are signalled in or parsed from a bitstream.
BRIEF DESCRIPTION OF THE DRAWINGS
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Fig. 1A illustrates an exemplary adaptive Inter/Intra video coding system incorporating loop processing.
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Fig. 1B illustrates a corresponding decoder for the encoder in Fig. 1A.
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Fig. 2 illustrates the ALF filter shapes for the chroma (left) and luma (right) components.
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Figs. 3A-D illustrates the subsampled Laplacian calculations for gv (3A) , gh (3B) , gd1 (3C) and gd2 (3D) .
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Fig. 4A illustrates the placement of CC-ALF with respect to other loop filters.
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Fig. 4B illustrates a diamond shaped filter for the chroma samples.
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Fig. 5 illustrates the 25-tap large filter used in CCALF process.
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Fig. 6 illustrates the diamond shaped ALF in ECM-5.0.
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Fig. 7 illustrates a longer ALF as an alternative to the diamond shaped ALF in Fig. 6.
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Fig. 8 illustrates the filter shape of ALF in ECM-7.0.
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Fig. 9 illustrates the filter shape of ALF with extended fixed-filter-output based taps.
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Fig. 10 illustrates ALF filter shape using residual samples as additional inputs.
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Fig. 11 illustrates a spatial part of the convolutional filter.
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Fig. 12 illustrates reference area (with its paddings) used to derive the filter coefficients.
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Fig. 13 illustrates four Sobel based gradient patterns for GLM.
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Fig. 14 illustrates non-downsampled luma samples for CCCM.
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Fig. 15 illustrates spatial samples used for GL-CCCM.
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Fig. 16 illustrates a flowchart of an exemplary video coding system that applies simplified ALF filtering process according to an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
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It will be readily understood that the components of the present invention, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following more detailed description of the embodiments of the systems and methods of the present invention, as represented in the figures, is not intended to limit the scope of the
invention, as claimed, but is merely representative of selected embodiments of the invention. References throughout this specification to “one embodiment, ” “an embodiment, ” or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment.
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Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. One skilled in the relevant art will recognize, however, that the invention can be practiced without one or more of the specific details, or with other methods, components, etc. In other instances, well-known structures, or operations are not shown or described in detail to avoid obscuring aspects of the invention. The illustrated embodiments of the invention will be best understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of apparatus and methods that are consistent with the invention as claimed herein.
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ALF with Taps Generated from Cross-Component Models (CCM)
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To utilize the chroma samples from applying cross-component model to luma samples to chroma ALF filtering process with signalled coefficients, the following steps can be applied:
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- Derive coefficients of luma-to-chroma cross-component model by reconstructed luma and chroma samples of neighbouring reference area and/or the current block
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- Derive chroma samples of the current block by applying luma-to-chroma cross-component model to luma samples of the current block
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- Chroma filtering process by signalled coefficients with the chroma samples and the chroma samples from the cross-component model
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The chroma-to-luma cross-component model can also be applied, that is, to utilize the luma samples from applying cross-component model to chroma samples to luma ALF or CCALF filtering process with signalled coefficients, the following steps can be applied:
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- Derive coefficients of chroma-to-luma cross-component model by reconstructed chroma and luma samples of neighbouring reference area and/or the current block
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- Derive luma samples of the current block by applying chroma-to-luma cross-component model to chroma samples of the current block
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- ALF luma or CCALF filtering process by signalled coefficients with the luma samples and the luma samples from the cross-component model
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The reconstructed luma and chroma samples of neighbouring reference area used for deriving coefficients of cross-component model can be samples before applying ALF filtering process or samples after applying ALF filtering process.
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ALF Complexity Reduction for Cross-Component Taps
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In some designs of ALF, cross-component samples are used. That is, Cb and/or Cr samples are used for luma ALF filtering, luma and/or Cr (Cb) samples are used for Cb (Cr) ALF filtering. In one embodiment, to reduce the complexity in terms of the number of ALF filtering taps, one or more of the following steps can be applied.
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Example:
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- Derive a model, which takes two cross components as input, and use the model output for ALF
filtering
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- Derive a cross-component model for each cross component (e.g., for luma ALF, derive a model for mapping Cb to Y, and a model for mapping Cr to Y) , and only use the cross component with the lower matching error for ALF filtering
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- Check the similarity between a cross component and the to-be-filtered component, and only use one of the cross components for ALF filtering based on the result of similarity check
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- Explicitly signal one flag to indicate which cross component is used for ALF filtering
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- Take the sum, average, weighted sum, weighted average, maximum, or minimum of two cross components, and use the result for ALF filtering
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Cross-Component Model Taps:
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In one embodiment, to reduce the complexity of coefficients derivation, sub-sampled samples can be used to derive coefficients, the following steps can be applied:
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Example:
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- Derive coefficients of luma-to-chroma cross-component model by sub-sampled reconstructed luma and chroma samples of neighbouring reference area and/or the current block
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- Derive chroma samples of the current block by applying luma-to-chroma cross-component model to luma samples of the current block
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- Chroma filtering process by signalled coefficients with the chroma samples and the chroma samples from cross-component model
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While reconstructed samples are used in the above example, prediction samples or residuals can be used. In one embodiment, the luma and chroma samples of neighbouring reference area and/or the current block used for deriving coefficients of cross-component model can be:
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- samples before applying ALF filtering process
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- samples after applying ALF filtering process
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- samples before applying deblocking filtering process
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- samples after applying deblocking filtering process
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- samples before applying SAO filtering process
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- samples after applying SAO filtering process
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- samples before applying bilateral filtering process
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- samples after applying bilateral filtering process
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- prediction samples
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- residual samples
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The samples for samples after applying ALF filtering process refer to the samples from a neighbouring area or luma samples of the current block. In above embodiment, the samples mentioned in the above embodiment can also be applied to the luma or chroma samples of the current block used for deriving samples from the cross-component model.
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Example:
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- Derive coefficients of luma-to-chroma cross-component model by reconstructed luma and chroma samples before applying the deblocking filtering process of neighbouring reference area and/or the current block
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- Derive chroma samples of the current block by applying luma-to-chroma cross-component model to luma samples before applying the ALF filtering process of current block
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- Chroma filtering process by signalled coefficients with the chroma samples and the chroma samples from the cross-component model
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Example:
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- Derive coefficients of luma-to-chroma cross-component model by reconstructed luma and chroma samples before applying the ALF filtering process of neighbouring reference area and/or the current block
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- Derive chroma samples of the current block by applying luma-to-chroma cross-component model to luma samples after applying the ALF filtering process of the current block
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- CCALF filtering process by signalled coefficients with the luma samples and the chroma samples from the cross-component model
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In one embodiment, the coefficients of cross-component model of neighbouring block can be used as the coefficients of cross-component model for the current block, instead of deriving coefficients of cross-component model for the current block.
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Example:
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- Use coefficients of luma-to-chroma cross-component model from above or left block as the coefficients of luma-to-chroma cross-component model for the current block
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- Derive chroma samples of current block by applying luma-to-chroma cross-component model to the luma samples of the current block
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- Chroma filtering process by signalled coefficients with the chroma samples and the chroma samples from the cross-component model
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In one embodiment, the coefficients of the cross-component model of current block can be signalled in the bitstream or set to pre-defined coefficients, instead of deriving coefficients of the cross-component model for the current block.
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The foregoing proposed methods can be implemented in encoders and/or decoders. For example, the proposed method can be implemented in an in-loop filtering module of an encoder, and/or an in-loop filtering module of a decoder.
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Any of the simplified ALF methods described above can be implemented in encoders and/or decoders. Also, any of the simplified ALF methods described above can be implemented in encoders and/or decoders. For example, any of the proposed methods can be implemented in the in-loop filter module (e.g. ILPF 130 in Fig. 1A and Fig. 1B) of an encoder or a decoder. Alternatively, any of the proposed methods can be implemented as circuits coupled to the inter coding module of an encoder and/or motion compensation module, a merge candidate derivation module of the decoder. The simplified ALF methods may also be implemented using executable software or firmware codes stored on a media, such as hard disk or flash memory, for a CPU (Central Processing Unit) or programmable devices (e.g. DSP (Digital Signal Processor) or FPGA (Field Programmable Gate Array) ) .
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Fig. 16 illustrates a flowchart of an exemplary video coding system that applies simplified ALF filtering process 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. According to the method, input data for a current block comprising a first-colour block and a second-colour block is received in step 1610, wherein the input data comprises reconstructed first-colour samples of the first-colour block
and reconstructed second-colour samples of the second-colour block. Model parameters for cross-component model is derived by using sub-sampled first-colour samples and sub-sampled second-colour samples in a neighbouring area of the current block, the current block, or both in step 1620. One or more cross-component second-colour samples are derived by applying the cross-component model with the model parameters derived to the reconstructed first-colour samples of the first-colour block in step 1630. Target ALF processing is applied to the current block to generate a filtered second-colour block in step 1640, wherein the target ALF processing comprises taps associated with said one or more cross-component second-colour samples. The filtered second-colour block is provided in step 1650.
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The flowchart shown is intended to illustrate an example of video coding according to the present invention. A person skilled in the art may modify each step, re-arranges the steps, split a step, or combine steps to practice the present invention without departing from the spirit of the present invention. In the disclosure, specific syntax and semantics have been used to illustrate examples to implement embodiments of the present invention. A skilled person may practice the present invention by substituting the syntax and semantics with equivalent syntax and semantics without departing from the spirit of the present invention.
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The above description is presented to enable a person of ordinary skill in the art to practice the present invention as provided in the context of a particular application and its requirement. Various modifications to the described embodiments will be apparent to those with skill in the art, and the general principles defined herein may be applied to other embodiments. Therefore, the present invention is not intended to be limited to the particular embodiments shown and described, but is to be accorded the widest scope consistent with the principles and novel features herein disclosed. In the above detailed description, various specific details are illustrated in order to provide a thorough understanding of the present invention. Nevertheless, it will be understood by those skilled in the art that the present invention may be practiced.
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Embodiment of the present invention as described above may be implemented in various hardware, software codes, or a combination of both. For example, 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. 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. However, 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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The invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described examples are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.