VVC achieves up to 50% bit-rate savings over HEVC at the cost of increased encoding complexity, largely due to the QTMTT partitioning structure. This work surveys the evolution of the VVC Test Model (VTM) and evaluates partitioning acceleration techniques considering changes in complexity and internal heuristics across VTM versions.

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 presents a comparison study of two machine-learning techniques to accelerate the Versatile Video Coding (VVC) intra-partitioning process in QuadTree (QT) configuration: a regression model for predicting Rate-Distortion (RD) costs and a Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach that models partitioning as a Markov Decision Process (MDP). Both methods are size-independent and utilize neighboring RD costs and threshold values to optimize the splits of Coding Units (CUs).

Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compression rates compared to the decades long traditional codecs such as AVC, HEVC or VVC. However, because of complexity and energy consumption, these approaches are still far away from practical usage in industry. More recently, implicit neural representation (INR) based codecs have emerged, and have lower complexity and energy usage to classical approaches at decoding. However, their performances are not in par at the moment with state-of-the-art methods. In this research, we first show that INR based image codec has a lower complexity than VAE based approaches, then we propose several improvements for INR-based image codec and outperformed baseline model by a large margin.

End-to-end image and video compression using auto-encoders (AE) offers new appealing perspectives in terms of rate-distortion gains and applications. While most complex models are on par with the latest compression standard like VVC/H.266 on objective metrics, practical implementation and complexity remain strong issues for real-world applications. We propose a practical implementation suitable for realistic applications. We demonstrate that some gains can be achieved on top low-complexity AE, even when using simpler implementation. The proposed implementation also allows a direct integration of such approaches on a variety of platforms and code is made available as a pure C++ standalone codec [1]:

Deep neural networks have been recently proposed to solve video interpolation tasks. Given a past and future frame, such networks can be trained to successfully predict the intermediate frame(s). In the context of video compression, these architectures could be useful as an additional inter-prediction mode. Current inter-prediction methods rely on block-matching techniques to estimate the motion between consecutive frames. This approach has severe limitations for handling complex non-translational motions, and is still limited to block-based motion vectors. This paper presents a deep frame interpolation network for video compression aiming at solving the previous limitations, i.e. able to cope with all types of geometrical deformations by providing a dense motion compensation. Experiments with the classical bi-directional hierarchical video coding structure demonstrate the efficiency of the proposed approach over the traditional tools of the HEVC codec.

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.

This paper describes the coding block structure used in the joint Qualcomm/Technicolor responses to the JVET call for proposal on video compression with capability beyond HEVC. The proposed block structure relies on the known quadtree plus binary tree (QTBT) block structure and brings some higher degree of flexibility in it. Two sets of partitioning modes extend QTBT: the ternary tree (TT) and the asymmetric binary tree (ABT). The added split modes are used with a normative non-redundancy policy, which ensures any block topology can be reached through a unique series of split modes. Finally, some flexible binary and ternary partitioning is allowed near the bottom and right picture borders. Overall, the proposed split modes, non-redundancy and picture border strategies lead to 19.6% bitrate reduction over HEVC.

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.

A new video compression scheme for High Dynamic Range (HDR) contents is presented. It is assumed that both Standard Dynamic Range (SDR) and HDR devices coexist in the ecosystem, and the scheme is constructed to ensure backward compatibility such that a single video stream carries both the original HDR video and an automatically generated SDR version of this video without noticeable overhead relatively to the distribution of the HDR video only. Compression performance are shown to be solidly improved compared to the conservative and non-backward-compatible approach using the HEVC Main10 codec and the Perceptual Quantizer (PQ) Electro-Optical Transfer Function (OETF) as non-linearity.