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<div style="font-size:3em; text-align:center;"> Algorithm Description </div>
# Abstract
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.
# Table of Contents
[Abbreviations](#Abbreviations)
[Algorithm description](#Algorithm-Description)
- [Block Partitioning](#Block-Partitioning)
- [Coding block partition](#Coding-block-partition)
- [Transform block partition](#Transform-block-partition)
- [Intra Prediction](#Intra-Prediction)
- [Directional intra prediction modes](#Directional-intra-prediction-modes)
- [Non-directional intra prediction modes](#Non-directional-intra-prediction-modes)
- [Recursive filtering modes](#Recursive-filtering-modes)
- [Chroma from Luma mode](#Chroma-from-Luma-mode)
- [Inter Prediction](#Inter-Prediction)
- [Motion vector prediction](#Motion-vector-prediction)
- [Motion vector coding](#Motion-vector-coding)
- [Interpolation filter for motion compensation](#Interpolation-filter-for-motion-compensation)
- [Warped motion compensation](#Warped-motion-compensation)
- [Overlapped block motion compensation](#Overlapped-block-motion-compensation)
- [Reference frames](#Reference-frames)
- [Compound Prediction](#Compound-Prediction)
- [Transform](#Transform)
- [Quantization](#Quantization)
- [Entropy Coding](#Entropy-Coding)
- [Loop filtering and post-processing](#Loop-filtering-and-post-processing)
- [Deblocking](#Deblocking)
- [Constrained directional enhancement](#Constrained-directional-enhancement)
- [Loop Restoration filter](#Loop-Restoration-filter)
- [Frame super-resolution](#Frame-super-resolution)
- [Film grain synthesis](#Film-grain-synthesis)
- [Screen content coding](#Screen-content-coding)
- [Intra block copy](#Intra-block-copy)
- [Palette mode](#Palette-mode)
[References](#References)
# Abbreviations
CfL: Chroma from Luma\
IntraBC: Intra block copy\
LCU: Largest coding unit\
OBMC: Overlapped Block Motion Compensation\
CDEF: Constrained Directional Enhancement Filter
# Algorithm Description
## Block Partitioning
### Coding block partition
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.
<figure class="image"> <center><img src="img\partition_codingblock.svg"
alt="Partition" width="360" /> <figcaption>Figure 1: Supported coding block
partitions</figcaption> </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.
### Transform block partition
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.
<figure class="image"> <center><figcaption>Table 1: Transform partition size
setting</figcaption> <img src="img\tx_partition.svg" alt="Partition" width="220"
/> </figure>
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.
<figure class="image"> <center><img src="img\intra_tx_partition.svg"
alt="Partition" width="600" /> <figcaption>Figure 2: Example of transform
partitioning for intra coded block</figcaption> </figure>
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.
<figure class="image"> <center><img src="img\inter_tx_partition.svg"
alt="Partition" width="400" /> <figcaption>Figure 3: Example of transform
partitioning for inter coded block</figcaption> </figure>
## Intra Prediction
### Directional intra prediction modes
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.
<figure class="image"> <center><img src="img\intra_directional.svg"
alt="Directional intra" width="300" /> <figcaption>Figure 4: Directional intra
prediction modes</figcaption> </figure>
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.
### Non-directional intra prediction modes
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.
<figure class="image"> <center><img src="img\intra_paeth.svg" alt="Directional
intra" width="300" /> <figcaption>Figure 5: Paeth predictor</figcaption>
</figure>
### Recursive filtering modes
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.
<figure class="image"> <center><img src="img\intra_recursive.svg"
alt="Directional intra" width="300" /> <figcaption>Figure 6: Recursive filtering
modes</figcaption> </figure>
### Chroma from Luma mode
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.
<figure class="image"> <center><img src="img\intra_cfl.svg" alt="Directional
intra" width="700" /> <figcaption>Figure 7: CfL prediction</figcaption>
</figure>
## Inter Prediction
### Motion vector prediction
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.
<figure class="image"> <center><img src="img\inter_spatial_mvp.svg"
alt="Directional intra" width="350" /><figcaption>Figure 8: Motion field
estimation by linear projection</figcaption></figure>
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
class="image"> <center><img src="img\inter_motion_field.svg" alt="Directional
intra" width="800" /><figcaption>Figure 9: Motion field estimation by linear
projection</figcaption></figure> 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 class="image"> <center><img
src="img\inter_tmvp_positions.svg" alt="Directional intra" width="300"
/><figcaption>Figure 10: Block positions for deriving temporal MV
predictors</figcaption></figure> 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.
### Motion vector coding
### Interpolation filter for motion compensation
<mark>[Ed.: to be added]</mark>
### Warped motion compensation
**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:
* Current block is single prediction
* Width or height is greater than or equal to 8 samples
* At least one of the immediate neighbors uses same reference frame with current block
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.
### Overlapped block motion compensation
For an inter-coded block, overlapped block motion compensation (OBMC) is allowed
when the following conditions are all satisfied.
* Current block is single prediction
* Width or height is greater than or equal to 8 samples
* At least one of the neighboring blocks are inter-coded blocks
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.
<figure class="image"> <center><img src="img\inter_obmc.svg" alt="Directional
intra" width="300" /><figcaption>Figure 11: neighboring blocks for OBMC
process</figcaption></figure>
### Reference frames
<mark>[Ed.: to be added]</mark>
### Compound Prediction
<mark>[Ed.: to be added]</mark>
**Compound wedge prediction**
<mark>[Ed.: to be added]</mark>
**Difference-modulated masked prediction**
<mark>[Ed.: to be added]</mark>
**Frame distance-based compound prediction**
<mark>[Ed.: to be added]</mark>
**Compound inter-intra prediction**
<mark>[Ed.: to be added]</mark>
## Transform
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.
<figure class="image"> <center><figcaption>Table 2: Transform basis functions
(DCT-2, DST-4 and DST-7 for N-point input.</figcaption> <img src=
"img\tx_basis.svg" alt="Partition" width="450" /> </figure>
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.
<figure class="image"> <center><figcaption>Table 3: Transform type candidates
for luma component when max(width, height) is greater than or equal to 32.
</figcaption> <img src="img\tx_cands_large.svg" alt="Partition" width="370" />
</figure>
<figure class="image"> <center><figcaption>Table 4: Transform type candidates
for luma component when max(width, height) is smaller than 32. </figcaption>
<img src="img\tx_cands_small.svg" alt="Partition" width="440" /> </figure>
The set of transform type candidates (namely transform set) is defined in Table
5.
<figure class="image"> <center><figcaption>Table 5: Definition of transform set.
</figcaption> <img src="img\tx_set.svg" alt="Partition" width="450" /> </figure>
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.
<figure class="image"> <center><figcaption>Table 6: Transform type selection for
chroma component intra prediction residuals.</figcaption> <img src=
"img\tx_chroma.svg" alt="Partition" width="500" /> </figure>
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:
1. The top-left 32×32 quadrant for 64×64/64×32/32×64 DCT_DCT hybrid transforms
2. The left 32×16 area for 64×16 and top 16×32 for16×64 DCT_DCT hybrid transforms.
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
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].
<figure class="image"> <center><img src="img\quant_dc.svg" alt="quant_dc"
width="800" /><figcaption>Figure 11: Quantization step size of DC coefficients
for different internal bit-depth</figcaption></figure>
<figure class="image"> <center><img src="img\quant_ac.svg" alt="quant_ac"
width="800" /><figcaption>Figure 12: Quantization step size of AC coefficients
for different internal bit-depth</figcaption></figure>
Given the quantization step size, indicated as _Q<sub>step_, the input quantized
coefficients is further de-quantized using the following formula:
_F_ = sign * ( (_f_ * _Q<sub>step_) % 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
**Entropy coding engine**
<mark>[Ed.: to be added]</mark>
**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.
## Loop filtering and post-processing
### Deblocking
There are four methods when picking deblocking filter level, which are listed
below:
* LPF_PICK_FROM_FULL_IMAGE: search the full image with different values
* LPF_PICK_FROM_Q: estimate the filter level based on quantizer and frame type
* LPF_PICK_FROM_SUBIMAGE: estimate the level from a portion of image
* LPF_PICK_MINIMAL_LPF: set the filter level to 0 and disable the deblocking
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.
### Constrained directional enhancement filter
**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.
<figure class="image"> <center><img src="img\edge_direction.svg"
alt="Edge direction" width="700" /> <figcaption>Figure 13: Line number
k for pixels following direction d=0:7 in an 8x8 block.</figcaption> </figure>
The optimal edge direction d_opt is found by maximizing the following
term [3]:
<figure class="image"> <center><img src="img\equ_edge_direction.svg"
alt="Equation edge direction" width="250" /> </figure>
<!-- $$d_{opt}=\max_{d} s_d$$
$$s_d = \sum_{k}\frac{1}{N_{d,k}}(\sum_{p\in P_{d,k}}x_p)^2,$$ -->
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).
<figure class="image"> <center><img src="img\primary_tap.svg"
alt="Primary tap" width="700" /> <figcaption>Figure 14: Primary filter
taps following edge direction. For even strengths a = 2 and b = 4, for
odd strengths a = 3 and b = 3. The filtered pixel is shown in the
highlighted center.</figcaption> </figure>
<figure class="image"> <center><img src="img\secondary_tap.svg"
alt="Edge direction" width="700" /> <figcaption>Figure 15: Secondary
filter taps. The filtered pixel is shown in the highlighted center.
</figcaption> </figure>
CDEF can be described by the following equation:
<figure class="image"> <center><img src="img\equ_dir_search.svg"
alt="Equation direction search" width="720" /> </figure>
<!-- $$y(i,j)=x(i,j)+round(\sum_{m,n}w^{(p)}_{d,m,n}f(x(m,x)-x(i,j),S^{(p)},
D)+\sum_{m,n}w^{(s)}_{d,m,n}f(x(m,x)-x(i,j),S^{(s)},D)),$$ -->
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.
### Loop Restoration filter
**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]:
1) The w x w filter window of is separated into horizontal and vertical w-tap
convolutions.
2) The horizontal and vertical filters are constrained to be symmetric.
3) 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]:
<figure class="image"> <center><img src="img\equ_dual_self_guided.svg"
alt="Equation dual self guided filter" width="300" /> </figure>
<!-- $$X_r = X + \alpha (X_1 - X) + \beta (X_2 - X)$$ -->
At encoder side, alpha and beta are computed using:
<figure class="image"> <center><img src="img\equ_dual_self_para.svg"
alt="Equation dual self guided filter parameter" width="220" /> </figure>
<!-- $${\alpha, \beta}^T = (A^T A) ^{-1} A^T b,$$ -->
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:
<figure class="image"> <center><img src="img\equ_guided_filter.svg"
alt="Equation guided filter" width="155" /> </figure>
<!-- $$y=Fx+G,$$ -->
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:
1) Compute mean u and variance d of pixels in a (2r + 1) x (2r + 1) window
around the pixel to be filtered.
2) For each pixel, compute f = d / (d + e); g = (1 - f)u.
3) 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.
### Frame super-resolution
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.
### Film grain synthesis
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.
## Screen content coding
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
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.
1) 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.
2) 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)
3) 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.
<figure class="image"> <center><img src="img\SCC_IntraBC.svg" alt="Intra block
copy" width="600" /> <figcaption>Figure 13: the prediction area for IntraBC mode
in one super block prediction</figcaption> </figure>
### Palette mode
# References
[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.