On Macroscopic Complexity and Perceptual Coding

The theoretical limits of 'lossy' data compression algorithms are considered. The complexity of an object as seen by a macroscopic observer is the size of the perceptual code which discards all information that can be lost without altering the perception of the specified observer. The complexity of this macroscopically observed state is the simplest description of any microstate comprising that macrostate. Inference and pattern recognition based on macrostate rather than microstate complexities will take advantage of the complexity of the macroscopic observer to ignore irrelevant noise

A Universal Scheme for Wyner–Ziv Coding of Discrete Sources

We consider the Wyner–Ziv (WZ) problem of lossy compression where the decompressor observes a noisy version of the source, whose statistics are unknown. A new family of WZ coding algorithms is proposed and their universal optimality is proven. Compression consists of sliding-window processing followed by Lempel–Ziv (LZ) compression, while the decompressor is based on a modification of the discrete universal denoiser (DUDE) algorithm to take advantage of side information. The new algorithms not only universally attain the fundamental limits, but also suggest a paradigm for practical WZ coding. The effectiveness of our approach is illustrated with experiments on binary images, and English text using a low complexity algorithm motivated by our class of universally optimal WZ codes

A Progressive Universal Noiseless Coder

The authors combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh's (1987) universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, they benefit from the “successive approximation” capabilities of TSVQ, thereby allowing progressive transmission of images, while retaining the ability to noiselessly encode images of unknown statistics in a provably asymptotically optimal fashion. Noiseless compression results are comparable to Ziv-Lempel and arithmetic coding for both images and finely quantized Gaussian sources

Comparação entre os algoritmos de codificação Huffman e Lempel-Ziv para compressão de sinais de áudio após a filtragem

The Huffman and Lempel-Ziv coding algorithms are extensively used in digital communications for data compression. Through this data compression it is possible to significantly decrease the file size, reducing data storage costs and making the systems faster and more efficient. In this work, it is studied which of these algorithms has the best compression performance for filtered and unfiltered audio signals. The metrics used to analyze each performance are: signal-to-noise ratio, average code length and compression ratio. Moreover, Matlab software is used to simulate the distinct scenarios presented here.Trabalho de Conclusão de Curso (Graduação)Os algoritmos de codificação Huffman e Lempel-Ziv são extensivamente utilizados em comunicações digitais para a compressão de dados. Por meio dessa compressão é possível reduzir significativamente o tamanho dos arquivos, possibilitando a redução de gastos com armazenamento de dados e tornando os sistemas mais rápidos e eficientes. Neste trabalho é estudado qual desses algoritmos tem o melhor desempenho de compressão para sinais de áudio filtrados e não filtrados. As métricas utilizadas para a análise de cada desempenho são: relação sinal ruído, comprimento médio de código e taxa de compressão. Em função disso, é utilizado o software Matlab para a simulação dos diferentes panoramas aqui apresentados

Compression of next-generation sequencing reads aided by highly efficient de novo assembly

We present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information, and sequences, effectively collapsing very large datasets to less than 15% of their original size with no loss of information. Availability: Quip is freely available under the BSD license from http://cs.washington.edu/homes/dcjones/quip

Error-resilient coding tools in MPEG-4.

by Cheng Shu Ling.Thesis submitted in: July 1997.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 70-71).Abstract also in Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Image Coding Standard: JPEG --- p.1Chapter 1.2 --- Video Coding Standard: MPEG --- p.6Chapter 1.2.1 --- MPEG history --- p.6Chapter 1.2.2 --- MPEG video compression algorithm overview --- p.8Chapter 1.2.3 --- More MPEG features --- p.10Chapter 1.3 --- Summary --- p.17Chapter Chapter 2 --- Error Resiliency --- p.18Chapter 2.1 --- Introduction --- p.18Chapter 2.2 --- Traditional approaches --- p.19Chapter 2.2.1 --- Channel coding --- p.19Chapter 2.2.2 --- ARQ --- p.20Chapter 2.2.3 --- Multi-layer coding --- p.20Chapter 2.2.4 --- Error Concealment --- p.20Chapter 2.3 --- MPEG-4 work on error resilience --- p.21Chapter 2.3.1 --- Resynchronization --- p.21Chapter 2.3.2 --- Data Recovery --- p.25Chapter 2.3.3 --- Error Concealment --- p.28Chapter 2.4 --- Summary --- p.29Chapter Chapter 3 --- Fixed length codes --- p.30Chapter 3.1 --- Introduction --- p.30Chapter 3.2 --- Tunstall code --- p.31Chapter 3.3 --- Lempel-Ziv code --- p.34Chapter 3.3.1 --- LZ-77 --- p.35Chapter 3.3.2 --- LZ-78 --- p.36Chapter 3.4 --- Simulation --- p.38Chapter 3.4.1 --- Experiment Setup --- p.38Chapter 3.4.2 --- Results --- p.39Chapter 3.4.3 --- Concluding Remarks --- p.42Chapter Chapter 4 --- Self-Synchronizable codes --- p.44Chapter 4.1 --- Introduction --- p.44Chapter 4.2 --- Scholtz synchronizable code --- p.45Chapter 4.2.1 --- Definition --- p.45Chapter 4.2.2 --- Construction procedure --- p.45Chapter 4.2.3 --- Synchronizer --- p.48Chapter 4.2.4 --- Effects of errors --- p.51Chapter 4.3 --- Simulation --- p.52Chapter 4.3.1 --- Experiment Setup --- p.52Chapter 4.3.2 --- Results --- p.56Chapter 4.4 --- Concluding Remarks --- p.68Chapter Chapter 5 --- Conclusions --- p.69References --- p.7