Performance of Lempel-Ziv compressors with deferred innovation

The noiseless data-compression algorithms introduced by Lempel and Ziv (LZ) parse an input data string into successive substrings each consisting of two parts: The citation, which is the longest prefix that has appeared earlier in the input, and the innovation, which is the symbol immediately following the citation. In extremal versions of the LZ algorithm the citation may have begun anywhere in the input; in incremental versions it must have begun at a previous parse position. Originally the citation and the innovation were encoded, either individually or jointly, into an output word to be transmitted or stored. Subsequently, it was speculated that the cost of this encoding may be excessively high because the innovation contributes roughly 1g(A) bits, where A is the size of the input alphabet, regardless of the compressibility of the source. To remedy this excess, it was suggested to store the parsed substring as usual, but encoding for output only the citation, leaving the innovation to be encoded as the first symbol of the next substring. Being thus included in the next substring, the innovation can participate in whatever compression that substring enjoys. This strategy is called deferred innovation. It is exemplified in the algorithm described by Welch and implemented in the C program compress that has widely displaced adaptive Huffman coding (compact) as a UNIX system utility. The excessive expansion is explained, an implicit warning is given against using the deferred innovation compressors on nearly incompressible data

Parallel data compression

Data compression schemes remove data redundancy in communicated and stored data and increase the effective capacities of communication and storage devices. Parallel algorithms and implementations for textual data compression are surveyed. Related concepts from parallel computation and information theory are briefly discussed. Static and dynamic methods for codeword construction and transmission on various models of parallel computation are described. Included are parallel methods which boost system speed by coding data concurrently, and approaches which employ multiple compression techniques to improve compression ratios. Theoretical and empirical comparisons are reported and areas for future research are suggested

Exclusive-or preprocessing and dictionary coding of continuous-tone images.

The field of lossless image compression studies the various ways to represent image data in the most compact and efficient manner possible that also allows the image to be reproduced without any loss. One of the most efficient strategies used in lossless compression is to introduce entropy reduction through decorrelation. This study focuses on using the exclusive-or logic operator in a decorrelation filter as the preprocessing phase of lossless image compression of continuous-tone images. The exclusive-or logic operator is simply and reversibly applied to continuous-tone images for the purpose of extracting differences between neighboring pixels. Implementation of the exclusive-or operator also does not introduce data expansion. Traditional as well as innovative prediction methods are included for the creation of inputs for the exclusive-or logic based decorrelation filter. The results of the filter are then encoded by a variation of the Lempel-Ziv-Welch dictionary coder. Dictionary coding is selected for the coding phase of the algorithm because it does not require the storage of code tables or probabilities and because it is lower in complexity than other popular options such as Huffman or Arithmetic coding. The first modification of the Lempel-Ziv-Welch dictionary coder is that image data can be read in a sequence that is linear, 2-dimensional, or an adaptive combination of both. The second modification of the dictionary coder is that the coder can instead include multiple, dynamically chosen dictionaries. Experiments indicate that the exclusive-or operator based decorrelation filter when combined with a modified Lempel-Ziv-Welch dictionary coder provides compression comparable to algorithms that represent the current standard in lossless compression. The proposed algorithm provides compression performance that is below the Context-Based, Adaptive, Lossless Image Compression (CALIC) algorithm by 23%, below the Low Complexity Lossless Compression for Images (LOCO-I) algorithm by 19%, and below the Portable Network Graphics implementation of the Deflate algorithm by 7%, but above the Zip implementation of the Deflate algorithm by 24%. The proposed algorithm uses the exclusive-or operator in the modeling phase and uses modified Lempel-Ziv-Welch dictionary coding in the coding phase to form a low complexity, reversible, and dynamic method of lossless image compression