Geometric Partitioning Mode (GPM) is an effective coding tool for inter prediction that splits a block into two partitions and blends their predictions. This paper presents a new coding mode, Regression-based Geometric Partitioning Mode (RGPM), which derives a sample-based blending for bi-predictions using a reconstructed template. The RGPM can enhance flexibility in splitting and blending methods compared to GPM. Moreover, two extensions of RGPM scheme are investigated: 1) extending RGPM with template matching (TM) and merge with motion vector difference (MMVD) methods; 2) extending RGPM principle to Spatial Geometric Partitioning Mode (SGPM) for intra prediction. Experimental results show that RGPM with extensions provide 0.12%, 0.24% and 0.23% average luma BD-rate savings on top of enhanced compression model (ECM) in all intra, random access and low delay configurations, respectively. The proposed RGPM is currently adopted in ECM and its two extensions are under study in exploration experiments for future ECM developments.

This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in intra slices. An encoding approach based on Convolutional Neural Networks is explored to partly substitute classical heuristics-based encoder speed-ups by a systematic and automatic process. The solution allows controlling the trade-off between complexity and coding gains, in intra slices, with one single parameter. This algorithm was proposed at the Call for Proposals of the Joint Video Exploration Team (JVET) on video compression with capability beyond HEVC. In All Intra configuration, for a given allowed topology of splits, a speed-up of ×2 is obtained without BD-rate loss, or a speed-up above ×4 with a loss below 1% in BD-rate.

Summary form only given. This paper presents two sets of modifications to band offset type of the Sample Adaptive Offset technique in HEVC. First, some constraints on the SAO semantics are added to solve sub-optimal syntax issue and to exploit the actual range information of reconstructed samples. Next, the classification process is adapted to the particular samples statistics.

This paper presents an adaptive clipping technique with optimized syntax in the video coding Joint Exploratory Model (JEM), which exploits the signal characteristics of the video sequence. The component-wise clipping bounds are coded for each slice. Two encoding methods leveraging the efficiency of the proposed technique are then described. The first one consists in modeling the errors induced by the clipping process in the Rate Distortion Optimization. The second one aims at reducing the cost of transform coefficients by smoothing the residuals. Finally, experimental results are provided and several variants are discussed.