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As the latest video coding standard, Versatile Video Coding (VVC) achieves up to 40% Bjøntegaard delta bit-rate (BD-rate) reduction compared with High Efficiency Video Coding (HEVC). Recently, Convolutional Neural Network (CNN) has attracted tremendous attention and shows great potential in video coding. In this paper, we design a Multi-Density Convolutional Neural Network (MDCNN) as an integrated in-loop filter to improve the quality of the reconstructed frames.

Lightweight neural network (LNN) nowadays plays a vital role in embedded applications with limited resources. Quantized LNN with a low bit precision is an effective solution, which further reduces the computational and memory resource requirements. However, it is still challenging to avoid the significant accuracy degradation compared with the heavy neural network due to its numerical approximation and lower redundancy. In this paper, we propose a novel robustness-aware self-reference quantization scheme for LNN (SRQ), as Fig.

COVID-19 has made video communication one of the most important modes of information exchange. While extensive research has been conducted on the optimization of the video streaming pipeline, in particular the development of novel video codecs, further improvement in the video quality and latency is required, especially under poor network conditions. This paper proposes an alternative to the conventional codec through the implementation of a keypoint-centric encoder relying on the transmission of keypoint information from within a video feed.

This paper introduces a dual-critic reinforcement learning (RL) framework to address the problem of frame-level bit allocation in HEVC/H.265. The objective is to minimize the distortion of a group of pictures (GOP) under a rate constraint. Previous RL-based methods tackle such a constrained optimization problem by maximizing a single reward function that often combines a distortion and a rate reward. However, the way how these rewards are combined is usually ad hoc and may not generalize well to various coding conditions and video sequences.

Bi-prediction is a fundamental module of inter prediction in the blocked-based hybrid video coding framework. Block-based motion estimation(ME) and motion compensation(MC) with simple models are adopted in bi-prediction process. Unfortunately, this MEMC-based algorithm can’t guarantee the prediction performance when it comes to videos with irregular motions. In this paper, a novel inter prediction scheme based on deep frame prediction network (DFP-net) is proposed to enhance bi-prediction accuracy, especially in complicated scenes.