Technically, all image data compression schemes can be categorized into two groups as lossless (reversible) and lossy (irreversible). Although some information is lost in the lossy compression, especially for the radiologic image compression, new algorithms can be designed to minimize the effect of data loss on the diagnostic features of the images. Wavelet transform (WT) constitute a new compr...

The concept of a loss-less data compression coding method is proposed, and a detailed description of each of its steps follows. Using the Calgary Corpus and Wikipedia data as the experimental samples and compared with existing algorithms, like PAQ or PPMstr, the new coding method could not only compress the source data, but also further re-compress the data produced by the other compression alg...

With the explosion of imaging applications, and due to the massive amounts of imagery data, data compression is essential. Lossless compression, also called entropy coding, is of special importance because not only it serves as a stand-alone system for certain applications such as medical imaging, it also is an inherent part of lossy compression. Therefore, fast entropy coding/decoding algorith...

This report presents some results and findings of our work on very-lowbit-rate video compression systems using vector quantization (VQ). We have identified multiscale segmentation and variable-rate coding as two important concepts whose effective use can lead to superior compression performance. Two VQ algorithms that attempt to use these two aspects are presented: one based on residual vector ...

Lossless data compression is a facet of source coding and a well studied problem of information theory. Its goal is to find a shortest possible binary code that can be unambiguously recovered. In this paper focus on rigorous analysis of code redundancy for known sources. The redundancy rate problem determines by how much the actual code length exceeds the optimal code length. We present precise...

This paper proposes a novel concept for digital image compression. The resulting compression scheme relies on adaptive thinning algorithms, which are recent multiresolution methods from scattered data approximation. Adaptive thinning algorithms are recursive point removal schemes, which are combined with piecewise linear interpolation over decremental Delaunay triangulations. This paper shows t...

Huffman coding is one of a most famous entropy encoding methods for lossless data compression [16]. JPEG and ZIP formats employ variants of Huffman encoding as lossless compression algorithms. Huffman coding is a bijective map from source letters into leaves of the Huffman tree constructed by the algorithm. In this article we formalize an algorithm constructing a binary code tree, Huffman tree.

Mixed raster content (MRC) is described in the following report. It is a compression method which will be described here for compression of compound images (images containing both, continuous tone images and binary text) [1]. It uses a multi-layer, multi-resolution representation of a compound document. Instead of using a single algorithm, it uses multiple compression algorithms, including the ...

Texture compression is a specialized form of image compression designed for storing texture maps in 3D computer graphics rendering systems. Texture compression is an important technique in graphics processing units (GPUs) for saving memory bandwidth. Unlike conventional image compression algorithms, texture compression algorithms are optimized for random access. Therefore, the variable-rate seq...