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This paper proposes a novel prediction tool for improving the compression performance of texture atlases. This algorithm, called Geometry-Aware (GA) intra coding, takes advantage of the topology of the associated 3D meshes, in order to reduce the redundancies in the texture map. For texture processing, the concept of the conventional intra prediction, used in video compression, has been adapted to consider neighboring information on the 3D surface. We have also studied how this prediction tool can be integrated into a complete coding solution. In particular, a block scanning strategy and a graph-based transform for residual coding have been proposed. Results show that the knowledge of the mesh topology significantly improves the compression efficiency of texture atlases 1 .

This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal. The motivation is that normative decisions made by the encoder can significantly impact the type and strength of artifacts in the decoded images. In this paper, the main focus has been put on decisions defining the prediction signal in intra and inter frames. This information has been used in the training phase as well as input to help the process of learning artifacts that are specific to each coding type. Furthermore, to retain a low memory requirement for the proposed method, one model is used for all Quantization Parameters (QPs) with a QP-map, which is also shared between luma and chroma components. In addition to the Post Processing (PP) approach, the In-Loop Filtering (ILF) codec integration has also been considered, where the characteristics of the Group of Pictures (GoP) are taken into account to boost the performance. The proposed CNN-based Quality Enhancement (QE) framework has been implemented on top of the Versatile Video Coding (VVC) Test Model (VTM-10). Experiments show that the prediction-aware aspect of the proposed method improves the coding efficiency gain of the default CNN-based QE method by 1.52%, in terms of BD-BR, at the same network complexity compared to the default CNN-based QE filter.