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Dictionary learning based image compression has attracted a lot of research efforts due to the inherent sparsity of image contents. Most algorithms in the literature, however, suffer from two drawbacks. First, the atoms selected for image patch reconstruction scatter over the entire dictionary, which leads to a high coding cost. Second, the sparse representation of image patches is performed independently from the quantization of sparse coefficients, which may result in a sub-optimal solution.

There is an increasing consumer demand for high bit-depth 4:4:4 HD video data playback due to its superior perceptual visual quality compared with standard 8-bit subsampled 4:2:0 video data. Due to vast file sizes and associated bitrates, it is desirable to compress raw high bit-depth 4:4:4 HD video sequences as much as possible without incurring a discernible decrease in visual quality. In this paper, we propose a Coding Block (CB)-level perceptual video coding technique for HEVC named Full Color Perceptual Quantization (FCPQ).

In this paper, a novel spatial resolution adaptation approach for video compression is proposed. Its ability to dynamically apply downsampling to frames exhibiting low spatial detail delivers improved rate distortion performance, together with a reduction in computational complexity of the encoding process. This method is based on an experimental investigation of the dependence between the QP threshold, which determines when to encode lower resolution frames, and the distortion obtained after downsampling/upsampling.

Interactive video communication has been recently proposed for multi-view videos. In this scheme, the server has to store the views as compact as possible, while being able to transmit them independently to the users, who are allowed to navigate interactively among the views, hence requesting a subset of them. To achieve this goal, the compression must be done using a model-based coding in which the correlation between the predicted view generated on the user side and the original view has to be modeled by a statistical distribution.

3D immersive communications are trending as real-time point
clouds capture and display of point cloud video become feasible.
This paper presents a novel motion-compensated approach to encoding
dynamic voxelized point clouds (VPC) at low bit rates. A simple
coder breaks the VPC into blocks which are intra-frame coded or
replaced by a motion-compensated version of a block in the previous
frame. The decision is optimized in a rate-distortion sense, encoding
with distortion both geometry and the color, at reduced bit-rates. Inloop

3D and free-viewpoint video has been moving towards solid-scene representation using meshes and point clouds. Pointcloud processing requires much less computation and the points in the cloud are minimally represented by their geometry (3D position) and color. A common point-cloud geometry compression method is the octree representation, which acts on individual frames. We present a lossless inter-frame compression method for pointcloud geometry, by reordering each octree based on previous frames prior to entropy coding.