The next generation Audio Video Coding Standard (AVS4) introduces enhanced intra prediction tools to further improve spatial coding efficiency. To improve directional adaptability for luma prediction and enhance cross-component correlation modeling for chroma, this paper proposes an intra prediction mode optimization method based on directional enhancement and cross-component fusion.

In search for more efficient video compression capability than versatile video coding (VVC), joint video experts team (JVET) launched exploration work on new video coding technologies beyond VVC in 2021. In 2023, audio video coding standard (AVS) workgroup started to explore the new video coding technologies beyond AVS3 and released a software platform named exploration video model (EVM). In EVM, the candidates of merge mode are reordered according to the template matching (TM) cost to reduce the candidate index signaling overhead. After reordering, the TM-based refinement is applied to the candidate to improve the accuracy of the motion derived in merge mode. However, it is found that the bi-prediction candidates are usually more effective than uni-prediction candidates despite the TM cost in non-low-delay pictures, while the current reordering method only relies on the TM cost. And when performing TM-based refinement, the motions of the two reference picture lists (RPLs) are processed independently without joint optimization. To solve these issues, an adaptive motion candidate reordering method and an improved TM motion refinement method which is based on the joint motion search are proposed in this paper. In the proposed candidate reordering method, the TM cost of uni-prediction candidate is weighted by a factor greater than 1 to prioritize the bi-prediction candidates. And in the proposed motion refinement method, the reference template of the bi-predicted block is obtained by averaging the templates of two predicted blocks, such that the motions of the two RPLs are jointly optimized. The proposed methods were implemented on top of EVM-0.9, it is reported that$\{-0.18 \%(\mathrm{Y}),-0.43 \%(\mathrm{U}),-0.08 \%(~\mathrm{V})\}$and$\{-0.03 \%(\mathrm{Y}),- 0.06 \%(\mathrm{U}), 0.18 \%(~\mathrm{V})\}$BD-rate reductions are achieved under random access and low-delay B configurations, respectively, with negligible runtime increase. Due to the good trade-off, the proposed methods have been adopted to EVM platform.

To meet the growing demand for advanced video coding, the Joint Video Experts Team (JVET) launched the Enhanced Compression Model (ECM) in April 2021 to explore technologies beyond Versatile Video Coding (VVC). Following this trend, the Audio Video Coding Standard (AVS) Working Group began exploring video coding technologies beyond AVS3 in 2023 and released the Exploration Video Model (EVM) as a development platform. In AVS3, the motion vector prediction (MVP) candidate list for the affine inter mode contains only one 4 -parameter spatial affine candidate, which severely limits its prediction accuracy. Furthermore, the MVD coding approach does not take advantage of the correlation between control points, which introduces redundancy in bitstream representation. To address these issues, this paper proposes an adaptive enhanced method. First, the number of MVP candidates for affine inter mode is extended from a single candidate to multiple ones, with an additional independent 6 -parameter candidate list. Inherited, constructed, and historical motion information are integrated to provide a richer set of prediction candidates. Second, a new adaptive coding method for MVD is designed to reduce bit overhead. Experimental results show that under the random access (RA) and low-delay B-picture (LDB) configurations, the proposed method achieves overall Bjøntegaard Delta Rate (BD-rate) reductions of$\{0.33 \%(\mathrm{Y}), 0.04 \%(\mathrm{U}), 0.34 \%(~\mathrm{V})\}$and$\{0.26 \%(\mathrm{Y}),-0.05 \% (\mathrm{U}), 0.40 \%(~\mathrm{V})\}$, respectively. Due to the excellent performance and acceptable computational complexity of specific components of the proposed scheme, these components have been integrated into the EVM platform as promising coding tools for the next generation AVS standard.

Audio video coding standard (AVS) workgroup started to explore the latest video coding technologies beyond AVS3 standard and released a software platform named exploration video model (EVM) in March 2023. In EVM-0.7, the construction of the motion vector (MV) candidate list for angle weighted prediction (AWP) only considers temporal and spatial neighboring motion information, ignoring non-adjacent candidates. And those motion candidates in the list are directly borrowed from previously coded blocks and thus may not match well with the current coding block. In this paper, we first propose an improved AWP MV candidate list construction method that incorporates more non-adjacent motion information. Second, we introduce an angle-adaptive refinement method to refine the motion candidate in the MV candidate list of AWP. The proposed method was implemented on top of EVM-0.7. And the experimental results show that it overall achieves$\{0.11 \%(\mathrm{Y}), 0.16 \%(\mathrm{U}),\ 0.14 \%(~\mathrm{V})$\} and$\{0.13 \%(\mathrm{Y}), 0.02 \%(\mathrm{U}), 0.13 \%(~\mathrm{V})\}$BD-rate gain on random access (RA) and low delay B (LDB) configurations, respectively, by applying the improved MV candidate list construction method, and$\{0.31 \%(\mathrm{Y}), 0.32 \%(\mathrm{U}),\ 0.43 \%(~\mathrm{V}))$and$\{0.27 \%(\mathrm{Y}), 0.05 \%(\mathrm{U}), 0.37 \%(~\mathrm{V})\}$BD-rate gain on RA and LDB configurations, respectively, by applying both the proposed MV candidate list construction method and the angle adaptive refinement method. Due to the attractive trade-off between performance and complexity, the improved MV candidate list construction was adopted into EVM software as the potential coding tool for the next generation of AVS standard.