This document describes technical aspects of coding tools included in the associated codec. This document is not a specification of the associated codec. Instead, it summarizes the highlighted features of coding tools for new developers. This document should be updated when significant new normative changes have been integrated into the associated codec.
CfL: Chroma from Luma
IntraBC: Intra block copy
LCU: Largest coding unit
OBMC: Overlapped Block Motion Compensation
CDEF: Constrained Directional Enhancement Filter
The largest coding block unit (LCU) applied in this codec is 128×128. In addition to no split mode PARTITION_NONE, the partition tree supports 9 different partitioning patterns, as shown in below figure.
According to the number of sub-partitions, the 9 partition modes are summarized as follows: 1. Four partitions: PARTITION_SPLIT, PARTITION_VERT_4, PARTITION_HORZ_4 2. Three partitions (T-Shape): PARTITION_HORZ_A, PARTITION_HORZ_B, PARTITION_VERT_A, PARTITION_HORZ_B 3. Two partitions: PARTITION_HORZ, PARTITION_VERT
Among all the 9 partitioning patterns, only PARTITION_SPLIT mode supports recursive partitioning, i.e., sub-partitions can be further split, other partitioning modes cannot further split. Particularly, for 8x8 and 128x128, PARTITION_VERT_4, PARTITION_HORZ_4 are not used, and for 8x8, T-Shape partitions are not used either.
For both intra and inter coded blocks, the coding block can be further partitioned into multiple transform units with the partitioning depth up to 2 levels. The mapping from the transform size of the current depth to the transform size of the next depth is shown in the following Table 1.
Furthermore, for intra coded blocks, the transform partition is done in a way that all the transform blocks have the same size, and the transform blocks are coded in a raster scan order. An example of the transform block partitioning for intra coded block is shown in the Figure 2.
For inter coded blocks, the transform unit partitioning can be done in a recursive manner with the partitioning depth up to 2 levels. The transform partitioning supports 1:1 (square), 1:2/2:1, and 1:4/4:1 transform unit sizes ranging from 4×4 to 64×64. If the coding block is smaller than or equal to 64x64, the transform block partitioning can only apply to luma component, for chroma blocks, the transform block size is identical to the coding block size. Otherwise, if the coding block width or height is greater than 64, then both the luma and chroma coding blocks will implicitly split into multiples of min(W, 64)x min(H, 64) and min(W, 32)x min(H, 32) transform blocks, respectively.
Directional intra prediction modes are applied in intra prediction, which models local textures using a given direction pattern. Directional intra prediction modes are represented by nominal modes and angle delta. The nominal modes are similar set of intra prediction angles used in VP9, which includes 8 angles. The index value of angle delta is ranging from -3 ~ +3, and zero delta angle indicates a nominal mode. The prediction angle is represented by a nominal intra angle plus an angle delta. In total, there are 56 directional intra prediction modes, as shown in the following figure. In the below figure, solid arrows indicate directional intra prediction modes and dotted arrows represent non-zero angle delta.
The nominal mode index and angle delta index is signalled separately, and nominal mode index is signalled before the associated angle delta index. It is noted that for small block sizes, where the coding gain from extending intra prediction angles may saturate, only the nominal modes are used and angle delta index is not signalled.
In addition to directional intra prediction modes, four non-directional intra modes which simulate smooth textures are also included. The four non-directional intra modes include SMOOTH_V, SMOOTH_H, SMOOTH and PAETH predictor.
In SMOOTH V, SMOOTH H and SMOOTH modes, the prediction values are generated using quadratic interpolation along vertical, horizontal directions, or the average thereof. The samples used in the quadratic interpolation include reconstructed samples from the top and left neighboring blocks and samples from the right and bottom boundaries which are approximated by top reconstructed samples and the left reconstructed samples.
In PAETH predictor mode, the prediction for each sample is assigned as one from the top (T), left (L) and top-left (TL) reference samples, which has the value closest to the Paeth predictor value, i.e., T + L -TL. The samples used in PAETH predictor are illustrated in below figure.
Five filtering intra modes are defined, and each mode specify a set of eight 7-tap filters. Given the selected filtering mode index (0~4), the current block is divided into 4x2 sub-blocks. For one 4×2 sub-block, each sample is predicted by 7-tap interpolation using the 7 top and left neighboring samples as inputs. Different filters are applied for samples located at different coordinates within a 4×2 sub-block. The prediction process can be done recursively in unit 4x2 sub-block, which means that prediction samples generated for one 4x2 prediction block can be used to predict another 4x2 sub-block.
Chroma from Luma (CfL) is a chroma intra prediction mode, which models chroma samples as a linear function of co-located reconstructed luma samples. To align the resolution between luma and chroma samples for different chroma sampling format, e.g., 4:2:0 and 4:2:2, reconstructed luma pixels may need to be sub-sampled before being used in CfL mode. In addition, the DC component is removed to form the AC contribution. In CfL mode, the model parameters which specify the linear function between two color components are optimized by encoder signalled in the bitstream.
Motion vectors are predicted by neighboring blocks which can be either spatial neighboring blocks, or temporal neighboring blocks located in a reference frame. A set of MV predictors will be identified by checking all these blocks and utilized to encode the motion vector information.
Spatial motion vector prediction
There are two sets of spatial neighboring blocks that can be utilized for finding spatial MV predictors, including the adjacent spatial neighbors which are direct top and left neighbors of the current block, and second outer spatial neighbors which are close but not directly adjacent to the current block. The two sets of spatial neighboring blocks are illustrated in an example shown in Figure 8.
For each set of spatial neighbors, the top row will be checked from left to right and then the left column will be checked from top to down. For the adjacent spatial neighbors, an additional top-right block will be also checked after checking the left column neighboring blocks. For the non-adjacent spatial neighbors, the top-left block located at (-1, -1) position will be checked first, then the top row and left column in a similar manner as the adjacent neighbors. The adjacent neighbors will be checked first, then the temporal MV predictor that will be described in the next subsection will be checked second, after that, the non-adjacent spatial neighboring blocks will be checked.
For compound prediction which utilizes a pair of reference frames, the non-adjacent spatial neighbors are not used for deriving the MV predictor.
Temporal motion vector prediction
In addition to spatial neighboring blocks, MV predictor can be also derived using co-located blocks of reference pictures, namely temporal MV predictor. To generate temporal MV predictor, the MVs of reference frames are first stored together with reference indices associated with the reference frame. Then for each 8x8 block of the current frame, the MVs of a reference frame which pass the 8x8 block are identified and stored together with the reference frame index in a temporal MV buffer. In an example shown in Figure 5, the MV of reference frame 1 (R1) pointing from R1 to a reference frame of R1 is identified, i.e., MVref, which passes a 8x8 block (shaded in blue dots) of current frame. Then this MVref is stored in the temporal MV buffer associated with this 8x8 block. Figure 9: Motion field estimation by linear projection Finally, given a couple of pre-defined block coordinates, the associated MVs stored in the temporal MV buffer are identified and projected accordingly to derive a temporal MV predictor which points from the current block to its reference frame, e.g., MV0 in Figure 5. In Figure 6, the pre-defined block positions for deriving temporal MV predictors of a 16x16 block are shown and up to 7 blocks will be checked to find valid temporal MV predictors. Figure 10: Block positions for deriving temporal MV predictors The temporal MV predictors are checked after the nearest spatial MV predictors but before the non-adjacent spatial MV predictors.
All the spatial and temporal MV candidates will be put together in a pool, with each predictor associated with a weighting determined during the scanning of the spatial and temporal neighboring blocks. Based on the associated weightings, the candidates are sorted and ranked, and up to four candidates will be used as a list MV predictor list.
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Global warped motion
The global motion information is signalled at each inter frame, wherein the global motion type and motion parameters are included. The global motion types and the number of the associated parameters are listed in the following table.
| Global motion type | Number of parameters |
|---|---|
| Identity (zero motion) | 0 |
| Translation | 2 |
| Rotzoom | 4 |
| General affine | 6 |
For an inter coded block, after the reference frame index is transmitted, if the motion of current block is indicated as global motion, the global motion type and the associated parameters of the given reference will be used for current block.
Local warped motion
For an inter coded block, local warped motion is allowed when the following conditions are all satisfied:
If the local warped motion is used for current block, instead of signalling the affine parameters, they are estimated by using mean square minimization of the distance between the reference projection and modeled projection based on the motion vectors of current block and its immediate neighbors. To estimate the parameters of local warped motion, the projection sample pair of the center pixel in neighboring block and its corresponding pixel in the reference frame are collected if the neighboring block uses the same reference frame with current block. After that, 3 extra samples are created by shifting the center position by a quarter sample in one or two dimensions, and these samples are also considered as projection sample pairs to ensure the stability of the model parameter estimation process.
For an inter-coded block, overlapped block motion compensation (OBMC) is allowed when the following conditions are all satisfied.
When OBMC is applied to current block, firstly, the initial inter prediction samples is generated by using the assigned motion vector of current block, then the inter predicted samples for the current block and inter predicted samples based on motion vectors from the above and left blocks are blended to generate the final prediction samples.The maximum number of neighboring motion vectors is limited based on the size of current block, and up to 4 motion vectors from each of upper and left blocks can be involved in the OBMC process of current block.
One example of the processing order of neighboring blocks is shown in the following picture, wherein the values marked in each block indicate the processing order of the motion vectors of current block and neighboring blocks. To be specific, the motion vector of current block is firstly applied to generate inter prediction samples P0(x,y). Then motion vector of block 1 is applied to generate the prediction samples p1(x,y). After that, the prediction samples in the overlapping area between block 0 and block 1 is an weighted average of p0(x,y) and p1(x,y). The overlapping area of block 1 and block 0 is marked in grey in the following picture. The motion vectors of block 2, 3, 4 are further applied and blended in the same way.
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Compound wedge prediction
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Difference-modulated masked prediction
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Frame distance-based compound prediction
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Compound inter-intra prediction
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The separable 2D transform process is applied on prediction residuals. For the forward transform, a 1-D vertical transform is performed first on each column of the input residual block, then a horizontal transform is performed on each row of the vertical transform output. For the backward transform, a 1-D horizontal transform is performed first on each row of the input de-quantized coefficient block, then a vertical transform is performed on each column of the horizontal transform output. The primary 1-D transforms include four different types of transform: a) 4-point, 8-point, 16-point, 32-point, 64-point DCT-2; b) 4-point, 8-point, 16-point asymmetric DST’s (DST-4, DST-7) and c) their flipped versions; d) 4-point, 8-point, 16-point, 32-point identity transforms. When transform size is 4-point, ADST refers to DST-7, otherwise, when transform size is greater than 4-point, ADST refers to DST-4.
For luma component, each transform block can select one pair of horizontal and vertical transform combination given a pre-defined set of transform type candidates, and the selection is explicitly signalled into the bitstream. However, the selection is not signalled when Max(width,height) is 64. When the maximum of transform block width and height is greater than or equal to 32, the set of transform type candidates depend on the prediction mode, as described in Table 3. Otherwise, when the maximum of transform block width and height is smaller than 32, the set of transform type candidates depend on the prediction mode, as described in Table 4.
The set of transform type candidates (namely transform set) is defined in Table 5.
For chroma component, the transform type selection is done in an implicit way. For intra prediction residuals, the transform type is selected according to the intra prediction mode, as specified in Table 4. For inter prediction residuals, the transform type is selected according to the transform type selection of the co-located luma block. Therefore, for chroma component, there is no transform type signalling in the bitstream.
The computational cost of large size (e.g., 64-point) transforms is further reduced by zeroing out all the coefficients except the following two cases:
Both the DCT-2 and ADST (DST-4, DST-7) are implemented using butterfly structure [1], which included multiple stages of butterfly operations. Each butterfly operations can be calculated in parallel and different stages are cascaded in a sequential order.
Quantization of transform coefficients may apply different quantization step size for DC and AC transform coefficients, and different quantization step size for luma and chroma transform coefficients. To specify the quantization step size, in the frame header, a base_q_idx syntax element is first signalled, which is a 8-bit fixed length code specifying the quantization step size for luma AC coefficients. The valid range of base_q_idx is [0, 255].
After that, the delta value relative to base_q_idx for Luma DC coefficients, indicated as DeltaQYDc is further signalled. Furthermore, if there are more than one color plane, then a flag diff_uv_delta is signaled to indicate whether Cb and Cr color components apply different quantization index values. If diff_uv_delta is signalled as 0, then only the delta values relative to base_q_idx for chroma DC coefficients (indicated as DeltaQUDc) and AC coefficients (indicated as DeltaQUAc) are signalled. Otherwise, the delta values relative to base_q_idx for both the Cb and Cr DC coefficients (indicated as DeltaQUDc and DeltaQVDc) and AC coefficients (indicated as DeltaQUAc and DeltaQVAc) are signalled.
The above decoded DeltaQYDc, DeltaQUAc, DeltaQUDc, DeltaQVAc and DeltaQVDc are added to base_q_idx to derive the quantization indices. Then these quantization indices are further mapped to quantization step size according to two tables. For DC coefficients, the mapping from quantization index to quantization step size for 8-bit, 10-bit and 12-bit internal bit depth is specified by a lookup table Dc_Qlookup[3][256], and the mapping from quantization index to quantization step size for 8-bit, 10-bit and 12-bit is specified by a lookup table Ac_Qlookup[3][256].
Given the quantization step size, indicated as Qstep, the input quantized coefficients is further de-quantized using the following formula:
F = sign * ( (f * Qstep) % 0xFFFFFF ) / deNorm
, where f is the input quantized coefficient, F is the output dequantized coefficient, deNorm is a constant value derived from the transform block area size, as indicated by the following table:
| deNorm | Tx block area size |
|---|---|
| 1 | Less than 512 samples |
| 2 | 512 or 1024 samples |
| 4 | Greater than 1024 samples |
When the quantization index is 0, the quantization is performed using a quantization step size equal to 1, which is lossless coding mode.
Entropy coding engine
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Coefficient coding
For each transform unit, the coefficient coding starts with coding a skip sign, which is followed by the signaling of primary transform kernel type and the end-of-block (EOB) position in case the transform coding is not skipped. After that, the coefficient values are coded in a multiple level map manner plus sign values. The level maps are coded as three level planes, namely lower-level, middle-level and higher-level planes, and the sign is coded as another separate plane. The lower-level, middle-level and higher-level planes correspond to correspond to different ranges of coefficient magnitudes. The lower level plane corresponds to the range of 0–2, the middle level plane takes care of the range of 3–14, and the higher-level plane covers the range of 15 and above.
The three level planes are coded as follows. After the EOB position is coded, the lower-level and middle-level planes are coded together in backward scan order, and the scan order refers to zig-zag scan applied on the entire transform unit basis. Then the sign plane and higher-level plane are coded together in forward scan order. After that, the remainder (coefficient level minus 14) is entropy coded using Exp-Golomb code.
The context model applied to the lower level plane depends on the primary transform directions, including: bi-directional, horizontal, and vertical, as well as transform size, and up to five neighbor (in frequency domain) coefficients are used to derive the context. The middle level plane uses a similar context model, but the number of context neighbor coefficients is reduced from 5 to 2. The higher-level plane is coded by Exp-Golomb code without using context model. For the sign plane, except the DC sign that is coded using the DC signs from its neighboring transform units, sign values of other coefficients are coded directly without using context model.
There are four methods when picking deblocking filter level, which are listed below:
When estimating the filter level from the full image or sub-image, the searching starts from the previous frame filter level, ends when the filter step is less or equal to zero. In addition to filter level, there are some other parameters which control the deblocking filter such as sharpness level, mode deltas, and reference deltas.
Deblocking is performed at 128x128 super block level, and the vertical and horizontal edges are filtered respectively. For a 128x128 super block, the vertical/horizontal edges aligned with each 8x8 block is firstly filtered. If the 4x4 transform is used, the internal edge aligned with a 4x4 block will be further filtered. The filter length is switchable from 4-tap, 6-tap, 8-tap, 14-tap, and 0-tap (no filtering). The location of filter taps are identified based on the number of filter taps in order to compute the filter mask. When finally performing the filtering, outer taps are added if there is high edge variance.
Edge Direction Estimation
In CDEF, edge direction search is performed at 8x8 block-level. There are eight edge directions in total, as illustrated in Figure 13.
The optimal edge direction d_opt is found by maximizing the following term [3]:
where x_p is the value of pixel p, P_{d,k} is the set of pixels in line k following direction d, N_{d,k} is the cardinality of P_{d,k}.
Directional filter
CDEF consists two filter taps: the primary tap and the secondary tap. The primary tap works along the edge direction (as shown in Figure 14), while the secondary tap forms an oriented 45 degree off the edge direction (as shown in Figure 15).
CDEF can be described by the following equation:
where x(i,j) and y(i,j) are the input and output reconstructed values of CDEF. p denotes primary tap, and s denotes secondary tap, w is the weight between primary and secondary tap. f(d,S,D) is a non-linear filtering function, S denotes filter strength, D is a damping parameter. For 8-bit content, S^p ranges from 0 to 15, and S^s can be 0, 1, 2, or 4. D ranges from 3 to 6 for luma, and 2 to 4 for chroma.
Non linear filter
CDEF uses a non-linear filtering function to prevent excessive blurring when applied across an edge. It is achieved by ignoring pixels that are too different from the current pixels to be filtered. When the difference between current pixel and it's neighboring pixel d is within a threshold, f(d,S,D) = d, otherwise f(d,S,D) = 0. Specifically, the strength S determines the maximum difference allowed and damping D determines the point to ignore the filter tap.
Separable symmetric wiener filter
Let F be a w x w 2D filter taps around the pixel to be filtered, denoted as a w^2 x 1 column vector. When compared with traditional Wiener Filter, Separable Symmetric Wiener Filter has the following three constraints in order to save signaling bits and reduce complexity [4]:
The w x w filter window of is separated into horizontal and vertical w-tap convolutions.
The horizontal and vertical filters are constrained to be symmetric.
It is assumed that the summation of horizontal/vertical filter coefficients is 1.
As a result, F can be written as F = column_vectorize[ab^T], subject to a(i) = a(w - 1 - i), b(i) = b(w - 1 - i), for i = [0, r - 1], and sum(a(i)) = sum(b(i)) = 1, where a is the vertical filters and b is the horizontal filters. The derivation of the filters a and b starts from an initial guess of horizontal and vertical filters, optimizing one of the two while holding the other fixed. In the implementation w = 7, thus, 3 taps need to be sent for filters a and b, respectively. When signaling the filter coefficients, 4, 5 and 6 bits are used for the first three filter taps, and the remaining ones are obtained from the normalization and symmetry constraints. 30 bits in total are transmitted for both vertical and horizontal filters.
Dual self-guided filter
Dual self-guided filter is designed to firstly obtain two coarse restorations X1 and X2 of the degraded frame X, and the final restoration Xr is obtained as a combination of the degraded samples, and the difference between the degraded samples and the coarse restorations [4]:
At encoder side, alpha and beta are computed using:
where A = {X1 - X, X2 - X}, b = Y - X, and Y is the original source.
X1 and X2 are obtained using guided filtering, and the filtering is controlled by a radius r and a noise parameter e, where a higher r implies a higher spatial variance and a higher e implies a higher range variance [4]. X1 and X2 can be described by {r1, e1} and {r2, e2}, respectively.
The encoder sends a 6-tuple {r1, e1, r2, e2, alpha, beta} to the decoder. In the implementation, {r1, e1, r2, e2} uses a 3-bit codebook, and {alpha, beta} uses 7-bit each due to much higher precision, resulting in a total of 17 bits. r is always less or equal to 3 [4].
Guided filtering can be described by a local linear model:
where x and y are the input and output samples, F and G are determined by the statistics in the neighboring of the pixel to be filtered. It is called self-guided filtering when the guidance image is the same as the degraded image[4].
Following are three steps when deriving F and G of the self-guided filtering:
Compute mean u and variance d of pixels in a (2r + 1) x (2r + 1) window around the pixel to be filtered.
For each pixel, compute f = d / (d + e); g = (1 - f)u.
Compute F and G for each pixel as averages of f and g values in a 3 x 3 window around the pixel for use in step 2.
In order to improve the perceptual quality of decoded pictures, a super-resolution process is applied at low bit-rates [5]. First, at encoder side, the source video is downscaled as a non-normative procedure. Second, the downscaled video is encoded, followed by deblocking and CDEF process. Third, a linear upscaling process is applied as a normative procedure to bring the encoded video back to it's original spatial resolution. Lastly, the loop restoration is applied to resolve part of the high frequency lost. The last two steps together are called super-resolving process [5]. Similarly, decoding, deblocking and CDEF processes are applied at lower spatial resolution at decoder side. Then, the frames go through the super-resolving process. In order to reduce overheads in line-buffers with respect to hardware implementation, the upscaling and downscaling process are applied to horizontal dimension only.
At encoder side, film grain is removed from the input video as a denoising process. Then, the structure and intensity of the input video are analyzed by canny edge detector, and smooth areas are used to estimate the strength of film grain. Once the strength is estimated, the denoised video and film grain parameters are sent to decoder side. Those parameters are used to synthesis the grain and add it back to the decoded video, producing the final output video.
In order to reconstruct the film grain, the following parameters are sent to decoder side: lag value, autoregressive coefficients, values for precomputed look-up table index of chroma components, and a set of points for a piece-wise linear scaling function [6]. Those parameters are signaled as quantized integers including 64 bytes for scaling function and 74 bytes for autoregressive coefficients. Once the parameters are received, an autoregressive process is applied in a raster scan order to generate one 64x64 luma and two 32x32 chroma film grain templates [6]. Those templates are used to generate the grain for the remaining part of a picture.
To improve the coding performance of screen content coding, the associated video codec incorporates several coding tools，for example, intra block copy (IntraBC) is employed to handle the repeated patterns in a screen picture, and palette mode is used to handle the screen blocks with a limited number of different colors.
Intra Block Copy (IntraBC) [2] is a coding tool similar to inter-picture prediction. The main difference is that in IntraBC, a predictor block is formed from the reconstructed samples (before application of in-loop filtering) of the current picture. Therefore, IntraBC can be considered as “motion compensation” within current picture.
A block vector (BV) was coded to specify the location of the predictor block. The BV precision is integer. The BV will be signalled in the bitstream since the decoder needs it to locate the predictor. For current block, the flag use IntraBC indicating whether current block is IntraBC mode is first transmitted in bit stream. Then, if the current block is IntraBC mode, the BV difference diff is obtained by subtracting the reference BV from the current BV, and then diff is classified into four types according to the diff values of horizontal and vertical component. Type information needs to be transmitted into the bitstream, after that, diff values of two components may be signalled based on the type info.
IntraBC is very effective for screen content coding, but it also brings a lot of difficulties to hardware design. To facilitate the hardware design, the following modifications are adopted.
when IntraBC is allowed, the loop filters are disabled, which are de-blocking filter, the CDEF (Constrained Directional Enhancement Filter), and the Loop Restoration. By doing this, picture buffer of reconstructed samples can be shared between IntraBC and inter prediction.
To facilitate parallel decoding, the prediction cannot exceed the restricted areas. For one super block, if the coordinate of its top-left position is (x0, y0), the prediction at position (x, y) can be accessed by IntraBC, if y < y0 and x < x0 + 2 * (y0 - y)
To allow hardware writing back delay, immediate reconstructed areas cannot be accessed by IntraBC prediction. The restricted immediate reconstructed area can be 1 ∼ n super blocks. So on top of modification 2, if the coordinate of one super block's top-left position is (x0, y0), the prediction at position (x, y) can be accessed by IntraBC, if y < y0 and x < x0 + 2 * (y0 - y) - D, where D denotes the restricted immediate reconstructed area. When D is one super block, the prediction area is shown in below figure.
[1] J. Han, Y. Xu and D. Mukherjee, “A butterfly structured design of the hybrid transform coding scheme,” 2013 Picture Coding Symposium (PCS), San Jose, CA, 2013, pp. 17-20.
[2] J. Li, H. Su, A. Converse, B. Li, R. Zhou, B. Lin, J. Xu, Y. Lu, and R. Xiong, “Intra Block Copy for Screen Content in the Emerging AV1 Video Codec,” 2018 Data Compression Conference, Snowbird, Utah, USA.
[3] S. Midtskogen and J.M. Valin. “The AV1 constrained directional enhancement filter (CDEF).” In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1193-1197. IEEE, 2018.
[4] D. Mukherjee, S. Li, Y. Chen, A. Anis, S. Parker, and J. Bankoski. “A switchable loop-restoration with side-information framework for the emerging AV1 video codec.” In 2017 IEEE International Conference on Image Processing (ICIP), pp. 265-269. IEEE, 2017.
[5] Y. Chen, D. Murherjee, J. Han, A. Grange, Y. Xu, Z. Liu,... & C.H.Chiang, (2018, June). “An overview of core coding tools in the AV1 video codec.”" In 2018 Picture Coding Symposium (PCS) (pp. 41-45). IEEE.
[6] A. Norkin, & N. Birkbeck, (2018, March). “Film grain synthesis for AV1 video codec.” In 2018 Data Compression Conference (pp. 3-12). IEEE.