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The extended Burrows-Wheeler-Transform (eBWT), introduced by Mantaci et al. [Theor. Comput. Sci., 2007], is a generalization of the Burrows-Wheeler-Transform (BWT) to multisets of strings. While the original BWT is based on the lexicographic order, the eBWT uses the omega-order, which differs from the lexicographic order in important ways. A number of tools are available that compute the BWT of string collections; however, the data structures they generate in most cases differ from the one originally defined, as well as from each other.

A universal scheme is proposed for the lossless compression of two-dimensional tables and matrices. Instead of standard row- or column-based compression, we propose to sort each column first and record both the sorted table and the corresponding permutation table of the sorting permutations. These two tables are then separately compressed. In this new scheme, both intra- and inter-column correlations can be efficiently captured, giving rise to improved compression ratio in particular when both column-wise and row-wise dependencies cooccur.

3-D point clouds rendering solid representations of scenes or objects often carry a tremendous amount of points, compulsorily requesting high-efficiency compression for storage and transmission. In this paper, we propose a novel p-Laplacian embedding graph dictionary learning algorithm for 3-D point cloud attribute compression. The proposed method integrates the underlying graph topology to the learned graph dictionary capitalizing on p-Laplacian eigenfunctions and leads to parsimonious representations of 3-D point clouds.

Existing methods for cross-component prediction focus on the chroma intra prediction but neglect the chroma prediction across multiple frames. In this paper, we propose a novel compression framework that leverages chroma frame sampling and inter-frame chroma prediction to address this problem. Specifically, chroma frame sampling discards the chroma components in inter-predicted frames to further reduce the bit consumption, whereas inter-frame chroma prediction recovers the discarded chroma components with optical flow estimation and post-training optimization for a guaranteed fidelity.

Recent advances in deep learning have led to superhuman performance across a variety of applications. Recently, these methods have been successfully employed to improve the rate-distortion performance in the task of image compression.

This paper proposes one new in-loop filtering technique cross-component sample adaptive offset (CCSAO) for further coding efficiency improvement beyond Versatile Video Coding (VVC). The CCSAO reduces the sample distortion by 1) utilizing the strong correlation between luma and chroma components to classify the reconstructed samples into different categories and 2) deriving one offset for each category and adding the offset to the samples in the category. The offset of each category is properly derived at encoder and signaled to decoder.