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Scalable lossless video coding is an important aspect for many professional applications. Wavelet-based video coding decomposes an input sequence into a lowpass and a highpass subband by filtering along the temporal axis. The lowpass subband can be used for previewing purposes, while the highpass subband provides the residual content for lossless reconstruction of the original sequence. The recursive application of the wavelet transform to the lowpass subband of the previous stage yields coarser temporal resolutions of the input sequence.

Video transrating has become an essential task in streaming service providers that need to transmit and deliver different versions of the same content for a multitude of users that operate under different network conditions. As the transrating operation is comprised of a decoding and an encoding step in sequence, a huge computational cost is required in such large-scale services, especially when considering the use of complex state-of-the-art codecs, such as the High Efficiency Video Coding (HEVC).

In video coding frameworks, the essence of intra coding is leveraging the spatial correlation within a frame to remove redundancy thus achieving compact transmitting data. With modern video acquisition devices improvement, more high-definition videos emerge into people’s lives which has set a new challenge for high-efficiency video coding. In this paper, we propose a novel intra video coding scheme based on Multiple Linear Regression (MLR), named Multiple linear regression Intra Prediction (MIP).

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