This article proposes a novel multi-view (MV) video coding technique that leverages a four-dimensional (4D) voxel-grid representation to enhance coding efficiency, particularly in novel view synthesis. Although the voxel grid approximation provides a continuous representation for dynamic scenes, its volumetric nature requires substantial storage. The compression of MV videos can be interpreted as the compression of dense features. However, the substantial size of these features poses a significant problem relative to the generation of dynamic scenes at arbitrary viewpoints. To address this challenge, this study introduces a hierarchical coded representation of dynamic volumes based on low-rank tensor decomposition of volumetric features and develops effective coding techniques based on this representation. The proposed method employs a two-level coding strategy to capture the temporal characteristics of the decomposed features. At a higher level, spatial features are encoded, representing 3D structural information, with time-invariant components over short intervals of an MV video sequence. At a lower level, temporal features are encoded to capture the dynamics of current scenes. The spatial features are shared in a group, and temporal features are encoded at each time step. The experimental results demonstrate that the proposed technique outperforms existing MV video coding standards and current state-of-the-art methods, providing superior rate-distortion performance in the novel view synthesis of MV video compression.

In this article, we propose an efficient reference-based deep in-loop filtering method for video coding. Existing reference-based in-loop filters often face challenges in improving coding efficiency due to the difficulty in capturing relevant textures from the reference frames. Our method accurately predicts the texture of a reference block and uses this information to restore the current block. To achieve this, we develop a reference-to-current feature estimation module that conveys high-quality information from previously coded frames in the feature domain, thereby preventing loss of detail due to inaccurate prediction. Although a neural network is trained to restore a coded video frame to be similar to the current frame, their performance can significantly degrade when operating with various quantization parameters (QPs) and managing different levels of distortion. This problem becomes further severe in the reference-to-current feature estimation, in which QP values are applied differently to video frames. We address this problem by developing a QP-aware convolution layer with a small number of learnable parameters to generate reliable features and adapt to fine-grained adaptive QPs among consecutive frames. The proposed method is implemented into the versatile video coding (VVC) reference software, VTM version 10.0. Experimental results demonstrate that the proposed method improves coding performance significantly in VVC.