Best-basis searching algorithm based on binary (in general, M-ary) segmentation was constructed by Coifman and Wickerhauser in 1992 [1] (IEEE Trans. on Information Theory, 38, 713-718). However, there are several problems with the binary scheme. First, the binary segmentation is inflexible in grouping signals along the axis. Secondly, the binary-based segmentation method is very sensitive to ti...

Finite context models improve the performance of chain based encoders to the point that they become attractive, alternative models for binary image compression. The resulting code is within 4% of JBIG at 200 dpi and is 9% more eecient at 400 dpi.

Image compression is achieved by removing data redundancy while preserving information content. SPIHT is computationally very fast and among the best image compression algorithms known today. According to statistic analysis of the output binary stream of SPIHT encoding, propose a simple and effective method combined with Entropy for further compression that saves a lot of bits in the image data...

The Lempel-Ziv-Welch (LZW) compression algorithm is widely used because it achieves an excellent compromise between compression performance and speed of execution. A simple way to improve the compression without significantly degrading its speed is proposed, and experimental data shows that it works in practice. Even better results are achieved with an additional optimization of “phasing in” bi...

A digital discrete signal corrsepond to a specific pointer is termed as bitplane image which represents position of bit in binary number. The lossless image compression is a combination of bitplane slicing and adaptive coding. This paper will discuss adaptive lossless image compression using bitplane slicing technique and derived the compression ratio. Refer ences

Data compression is concerned with how information is organized in data. Efficient storage means removal of redundancy from the data being stored in the DNA molecule. Data compression algorithms remove redundancy and are used to understand biologically important molecules. We present a compression algorithm, "DNABIT Compress" for DNA sequences based on a novel algorithm of assigning binary bits...

Data compression algorithms have been widely used in many areas to meet the demand of storage and transfer of large size data. Most of the data compression algorithms regard the input as a sequence of binary numbers and represent the compressed data also as a binary sequence. However, in many areas such as programming languages (e.g. LISP and C) and compiler design, it is more desirable to have...

Grammar-based compression algorithms infer context-free grammars to represent the input data. The grammar is then transformed into a symbol stream and finally encoded in binary. We explore the utility of grammar-based compression of DNA sequences. We strive to optimize the three stages of grammar-based compression to work optimally for DNA. DNA is notoriously hard to compress, and ultimately, o...

Recently Gao et al. proposed a wavelet-based image quality metric, based on which, a quality constrained image subband quantization scheme is derived for lossy compression. But this scheme needs entropy coding to find the compression ratio decrement for tuning the final quantization step of each subband, which is time consuming and wasteful. In addition, computing the initial quantization steps...