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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.

This paper presents a compression framework for light-field images. The main idea of our approach is exploiting the similarity across sub-aperture images extracted from light-field data to improve encoding performance. For this purpose we propose a variational optimisation approach to estimate the disparity map from light-field images and then apply it to a motion-compensated wavelet lifting scheme. Making use of JPEG2000 for coding all high-/low-pass sub-band views as well as disparity map, our approach can therefore support both lossless and lossy compression.

This paper addresses the problem of image compression using
sparse representations. We propose a variant of autoencoder
called Stochastic Winner-Take-All Auto-Encoder
(SWTA AE). “Winner-Take-All” means that image patches
compete with one another when computing their sparse representation
and “Stochastic” indicates that a stochastic hyperparameter
rules this competition during training. Unlike
auto-encoders, SWTA AE performs variable rate image compression
for images of any size after a single training, which

Existed inherent sparsity of natural images in some domains helps to reconstruct the signal with a lower number of measurements. To benefit from the sparsity, one should solve the reweighted $\ell_{1}$-norm minimization algorithms. Although, the existed reweighted $\ell_{1}$-norm minimization approaches work well for k-sparse signals, but, the performance of these methods for compressible signals are not competitive with unweighted one.

Recently, multidimensional signal reconstruction using a low number of measurements is of great interest. Therefore, an effective sampling scheme which should acquire the most information of signal using a low number of measurements is required. In this paper, we study a novel cube-based method for sampling and reconstruction of multidimensional signals. First, inspired by the block-based compressive sensing (BCS), we divide a group of pictures (GoP) in a video sequence into cubes. By this way, we can easily store the measurement matrix and also easily can generate the sparsifying basis.