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With the boosting requirements of realistic 3D modeling for immersive applications, advent of the newly-developed 3D point cloud has attracted great attention. Frankly, immersive experience using high data volume affirms the importance of efficient compression. Inspired by the video-based point cloud compression (V-PCC), we propose a novel point cloud compression algorithm based on polynomial fitting of proper patches. Moreover, the original point cloud is segmented into various patches.

Most covariance-based representations of actions are focused on the statistical features of poses by empirical averaging weighting. Note that these poses have a variety of saliency levels for different actions. Neglecting pose saliency could degrade the discriminative power of the covariance features, and further reduce the performance of action recognition. In this paper, we propose a novel saliency weighting covariance feature representation, Saliency-Pose-Attention Covariance(SPA-Cov), which reduces the negative effects from the ambiguous pose samples.

Machine learning for CRF-Rate modeling in transcoding

It is well-known that the application of the Discrete Cosine Transform
(DCT) in transform coding schemes is justified by the fact that
it belongs to a family of transforms asymptotically equivalent to the
Karhunen-Loève Transform (KLT) of a first order Markov process.
However, when the pixel-to-pixel correlation is low the DCT does
not provide a compression performance comparable with the KLT.
In this paper, we propose a set of symmetry-based Graph Fourier
Transforms (GFT) whose associated graphs present a totally or partially

In this work, we derive the rate-distortion function for video coding using affine global motion compensation.
We model the displacement estimation error during motion estimation and obtain the bit rate after applying the rate-distortion theory.
We assume that the displacement estimation error is caused by a perturbed affine transformation.
The 6 affine transformation parameters are assumed statistically independent, with each of them having a zero-mean Gaussian distributed estimation error.

As the compression efficiency of HEVC comes at the cost of high complexity, especially in the encoder’s side, improved parallelization techniques that will speedup the encoding process are essential. One of the parallelization granules offered by HEVC is the tile level, whereby a frame is split into a grid like fashion with each resulting rectangular area (tile) being independently encoded. While tile parallelism has attracted research interest, the primary focus was to characterize performance and develop load balancing schemes assuming a one on one tile processor assignment.

Dictionary learning is a popular method for obtaining sparse linear representations for high dimensional data, with many applications in image classification, signal processing and machine learning.