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In this paper, we propose a novel low-rank based non-local image denoising method for HEVC video compression with the strategy of gathering non-local patches in the rectified domain. Owing to the irreversible quantization, image compression can be considered as adding noises into the original image, causing the distortion between the original image and the de-compressed image.

Lossless data compression algorithms were developed to shrink files. But these algorithms can also be used to measure file similarity. In this article, the meta-algorithms Concat Compress and Cross Compress are subjected to an extensive practical test together with the compression algorithms Re-Pair, gzip and bz2: Five labeled datasets are subjected to a classification procedure using these algorithms. Theoretical considerations about the two meta-algorithms were already made about 10 years ago, but little has happened since then.

Linear computation coding (LCC) has been developed in as a new framework
for the computation of linear functions. LCC significantly reduces the complexity
of matrix-vector multiplication. In basic LCC, storage is not restricted i.e. the
wiring exponents are arbitrary integer exponents of 2.
In this work, we constrain the set of wiring exponents to be finite. From an
information-theoretic viewpoint, this problem is function compression with a finite-alphabet
representation by atoms. We show that by an efficient choice of this set,

We introduce an extension of the Burrows-Wheeler transform supporting exact pattern matching on a string graph $G(V,E,N)$ and yielding polynomial time queries.