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US-20260129201-A1 - METHODS OF PERFORMING ENTROPY CODING SCHEME FOR SIGNAL ENHANCEMENT FILTER, DECODER, AND STORAGE MEDIUM

US20260129201A1US 20260129201 A1US20260129201 A1US 20260129201A1US-20260129201-A1

Abstract

A method of performing an entropy decoding scheme for a signal enhancement filter is provided. The method includes: bitstream is parsed and entropy decoding of filter parameters is performed; filter parameter decoding is performed; and filter parameter inverse quantization is performed.

Inventors

  • Tim CLASSEN
  • Mathias Wien

Assignees

  • GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP., LTD.

Dates

Publication Date
20260507
Application Date
20251229

Claims (20)

  1. 1 . A method of performing an entropy coding scheme for a signal enhancement filter, performed by an encoder, the method comprising: optimizing parameter encoding based on estimated rate distortion costs by estimating rate distortion enhancement for a current frame and one or more subsequent frames.
  2. 2 . The method of claim 1 , wherein estimating rate distortion enhancement comprises calculating the rate distortion enhancement and summing it up for the one or more subsequent frames which have the current frame in their reference picture list.
  3. 3 . The method of claim 1 , further comprising performing filter parameter quantization; performing filter parameter coding; and performing entropy coding of the filter parameters.
  4. 4 . A method of performing an entropy decoding scheme for a signal enhancement filter, performed by a decoder, the method comprising: parsing bitstream and performing entropy decoding of filter parameters; performing filter parameter decoding; and performing filter parameter inverse quantization.
  5. 5 . The method of claim 4 , wherein filter parameter decoding is performed using a new filter decoding mode, a new filter intra decoding mode, a new filter inter decoding mode or a copy filter decoding mode, wherein each of these modes aims at a prediction of filter coefficients and/or filter parameters.
  6. 6 . The method of claim 4 , wherein filter parameters comprise at least one of filter coefficients, decoding parameters, region partitioning information, weighting map parameters or other side information.
  7. 7 . The method of claim 5 , wherein the new filter decoding mode is selected if the filter is not well predictable by any of the other decoding modes.
  8. 8 . The method of claim 5 , wherein the new filter intra decoding mode predicts filter coefficients based on other filter coefficients of the same filter and makes use of dependencies between filter coefficients of the same filter.
  9. 9 . The method of claim 5 , wherein the new filter inter decoding mode performs prediction of filter coefficients of a next filter based on filter coefficients of a previous filter.
  10. 10 . The method of claim 5 , wherein the copy filter coding mode copies all filter coefficients and is used to apply the same filter to another frame.
  11. 11 . The method of claim 5 , wherein the new filter inter decoding mode and the copy filter decoding mode make use of filter parameters from previous filters to predict a current filter, wherein the filter from which the current filter is predicted is indicated by an index and filters in a reference list are filters which have been transmitted for some previously coded frame.
  12. 12 . The method of claim 4 , wherein the filter parameters are determined from an adaption parameter set and a systematic code is used to perform the entropy decoding of the filter parameters.
  13. 13 . The method of claim 12 , wherein simple structured codes are used to perform the entropy decoding of the filter parameters.
  14. 14 . The method of claim 12 , wherein exponential Golomb-codes, Golomb-Rice codes or k-th order exponential Golomb codes are used to perform the entropy decoding on the filter parameters.
  15. 15 . The method of claim 12 , wherein separate chroma and luma filters are used.
  16. 16 . The method of claim 15 , wherein on-/off-flags for the luma and chroma filters are used.
  17. 17 . The method of claim 4 , wherein the method is used for an adaptive reference picture upsampling scheme.
  18. 18 . The method of claim 4 , wherein the method is used after an interpolation filter and before any other processing steps.
  19. 19 . A non-transitory computer-readable medium, having a computer program and a bitstream stored thereon, wherein the computer program, when executed by a processor, enables the processor to perform the method of claim 1 to generate the bitstream.
  20. 20 . A decoder, comprising one or more processors; and a computer-readable medium comprising computer executable instructions stored thereon which when executed by the one or more processors cause the one or more processors to perform operations of: parsing bitstream and performing entropy decoding of filter parameters; performing filter parameter decoding; and performing filter parameter inverse quantization.

Description

CROSS REFERENCE TO RELATED APPLICATION This is a continuation of International Application No. PCT/CN2023/105596 filed on Jul. 3, 2023, the disclosure of which is hereby incorporated by reference in its entirety. TECHNICAL FIELD The present application relates to the field of computer vision, in particular to the topic of video processing and video coding, more particularly to methods of performing an entropy coding scheme for a signal enhancement filter, a decoder, and a computer-readable medium. BACKGROUND Current video coding schemes such as H.265/HEVC (High Efficiency Video Coding) and H.266/VVC (Versatile Video Coding) support spatial scalability of the coded video stream. Adaptively changing the resolution of the coded video during coding is known from VVC as reference picture resampling (RPR) or adaptive resolution change (ARC). Moreover, multiple-resolution coding and multi-layer coding allows for a scalable resolution of the coded video. For that reason, the spatial resolution at which a video is coded may change adaptively and no longer needs to be equivalent to the output or input resolution of the video. The advantages of this additional flexibility are that coding a lower resolution video requires a lower bitrate and may reduce computational complexity at the cost of losing high frequency information in the downsampling step. Coding a video at lower resolution than its original resolution requires a downsampling and an upsampling step in the signal processing chain. In the downsampling step, an anti-aliasing filter is applied to prevent artifacts caused by high frequency components in the image. The upsampling process applies interpolation filters to reconstruct the intensity values at fractional sample positions. In RPR, the resolution of the coded video stream may change adaptively. Consequently, the encoder may code parts of the video stream at lower resolution. RPR is applied in the inter-prediction every time that a picture uses a reference picture of different resolution than the current picture in inter frame prediction. In this step, a resampling operation needs to be applied such that the referenced picture block is mapped to the same spatial resolution as the current picture. In multi-layer coding, the video is coded at different resolution layers. In a first step, the video is coded at the lowest resolution layer. To generate the video stream of the next layer, the video is upsampled and, potentially, a residual is coded and further processing steps are applied. This process may be applied multiple times based on the number of layers. Finding an optimal high-resolution representation from the low-resolution picture is an important part of the above-mentioned coding schemes. A common method is to apply a set of multi-phase Finite Impulse Response (FIR)-interpolation filters. While those filters do provide a good approximation of the high-resolution image content, they cannot recover information that was lost in the downsampling process and suffer from limitations of the linear filtering operation. Consequently, upsampled images are usually blurred. Therefore, an image sharpening operation could increase the picture quality. However, linear high-pass filters frequently cause artifacts such as overshoot and ringing. Moreover, the distortions caused by the down- and upsampling depend on the image content and the coding quality of the video (influenced by the Quantization Parameter (QP) value). Applying an adaptive filter with local weighting is an approach to deal with those problems. The local weighting can be applied to smoothly increase or decrease the strength of the filter at local regions. One could think of a weighting that increases the filter strength at edge regions but decreasing it at regions where ringing would typically occur. With such a setup, an optimized filter could amplify high frequency components without causing significant ringing. This is especially helpful in an image upsampling scenario where the amplification of high frequency components is required to sharpen blurred edges. Adaptive filters are required to deal with the different characteristics of coding artifacts and video content. The presented approach requires some side information to be sent. Those are some flags, filter coefficients, region partitioning information and mode parameters. For video coding applications it is substantial to minimize the required transmission rate. This means that the additional number of bits should be minimized. To reduce the number of bits, a coefficient coding scheme is proposed which exploits redundancies in the information which needs to be transmitted. BRIEF DESCRIPTION OF THE DRAWINGS Embodiments will now be described, by way of example only, with reference to the accompanying drawings, in which: FIG. 1A shows a flowchart of the operations of a method of performing an entropy coding scheme, performed by an encoder, for a signal enhancement filter according to embodime