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This work extends the multiscale structure originally developed for point cloud geometry compression to point cloud attribute compression. To losslessly encode the attribute while maintaining a low bitrate, accurate probability prediction is critical. With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency.

Decoder-side intra mode derivation (DIMD) is a promising coding tool in the enhanced compression model (ECM) developed by the joint video experts team (JVET). In DIMD, the intra prediction mode of a luma block is derived based on the gradient information of the adjacent luma samples at both encoder and decoder, rather than being explicitly signaled in the bitstream. Inspired by DIMD, a decoder-side chroma intra mode derivation (DCIMD) method is proposed in this paper to improve the coding efficiency of chroma intra prediction.

In Versatile Video Coding (VVC), Cross-component Linear Model (CCLM) predicts chroma samples by assuming a linear relationship between luma and chroma components. In performing CCLM for video in YUV 4:2:0 chroma format, collocated luma samples are firstly downsampled by a low-pass filter to match luma resolution with chroma, and one linear model of luma-chroma sample pairs is applied on the reconstructed luma samples to generate the predicted chroma samples.

This paper presents the new profile to supersede the existing Geometry Absent profile supported in the MPEG immersive video (MIV) coding standard. The proposed MIV Extended Decoder-Side Depth Estimation (MIV DSDE) profile was developed to cover more diverse use cases and applications based on the decoder-side depth estimation scheme and allow for further improvements of the efficiency of incoming MIV ed. 2, even after the standard will reach its final stage.

Most video platforms provide video streaming services with different qualities, and the resolution of the videos usually adjusts the quality of the services. So high-resolution videos need to be downsampled for compression. In order to solve the problem of video coding at different resolutions, we propose a rate-guided arbitrary rescaling network (RARN) for video resizing before encoding.

Since the distortions on simple pixels are more noticeable than those on complex pixels, this paper proposes a pixel-wise quantization method, which allows to reduce the quantization parameters of simple pixels adaptively for the purpose of enhancing the subjective quality, and no additional syntax need to be transmitted in the bitstream. The discrimination of simple pixels is based on the texture complexity of the neighboring reconstruction pixels. The reduction of quantization parameters is related to the just noticeable difference for the human visual system.

Restoring high-quality videos from low-quality compressed ones is a crucial research topic in video coding. Most existing methods do not exploit the information in the long-distance compressed frames. Even when they do, these methods ignore the effect of interference information during reconstruction. In this paper, we propose a unique Long-distance Information Filtering (LIF) scheme with the 3D-CNN, which enhances compressed videos by mining filtered and valid information from long-distance frames.