Gray Coding

This paper presents CRIC (Cross-Point Regions for Lossless Image Compression), a scheme for losslessly encoding and decoding images, especially medical images, by optimizing on the probability of cross points that neighbor points of grey levels 2 n. The base of this statement is the effect of Gray coding on cross points. At first, the effect of Gray codes is determined on an adjacent data set because images characteristically contain data that does not change much in a specific area; then this effect is generalized for real data without losing generality for their statistical properties. This is especially true for medical images that have many regions with the same grey levels. The Gray code transformation makes the bit states of cross points change from the original data bits, so first the probabilities of data bits on specific bit planes in cross point regions and then the entropies of the messages are changed. These probabilities are estimated and compared with the probabilities of the original data bits. This change of probability has important effects on the encoding and decoding processes in lossless medical image compression. Index Terms-cross point, entire cross point region, ideal cross point region, probability of bits

This paper presents CRIC (Cross-Point Regions for Lossless Image Compression), a scheme for losslessly encoding and decoding images, especially medical images, by optimizing on the probability of cross points that neighbor points of grey levels 2 n. The base of this statement is the effect of Gray coding on cross points. At first, the effect of Gray codes is determined on an adjacent data set because images characteristically contain data that does not change much in a specific area; then this effect is generalized for real data without losing generality for their statistical properties. This is especially true for medical images that have many regions with the same grey levels. The Gray code transformation makes the bit states of cross points change from the original data bits, so first the probabilities of data bits on specific bit planes in cross point regions and then the entropies of the messages are changed. These probabilities are estimated and compared with the probabilities of the original data bits. This change of probability has important effects on the encoding and decoding processes in lossless medical image compression. Index Terms-cross point, entire cross point region, ideal cross point region, probability of bits

This paper presents CRIC (Cross-Point Regions for Lossless Image Compression), a scheme for losslessly encoding and decoding images, especially medical images, by optimizing on the probability of cross points that neighbor points of grey levels 2 n. The base of this statement is the effect of Gray coding on cross points. At first, the effect of Gray codes is determined on an adjacent data set because images characteristically contain data that does not change much in a specific area; then this effect is generalized for real data without losing generality for their statistical properties. This is especially true for medical images that have many regions with the same grey levels. The Gray code transformation makes the bit states of cross points change from the original data bits, so first the probabilities of data bits on specific bit planes in cross point regions and then the entropies of the messages are changed. These probabilities are estimated and compared with the probabilities of the original data bits. This change of probability has important effects on the encoding and decoding processes in lossless medical image compression. Index Terms-cross point, entire cross point region, ideal cross point region, probability of bits

Increasing requirements for data transfer and storage is one of the crucial questions now. There are several ways of high-speed data transmission, but they meet limited requirements applied to their narrowly focused specific target. The data compression approach gives the solution to the problems of high-speed transfer and low-volume data storage. This paper is devoted to the compression of GIF images, using a modified LZW algorithm with a tree-based dictionary. It has led to a decrease in lookup time and an increase in the speed of data compression, and in turn, allows developing the method of constructing a hardware compression accelerator during the future research.

A new chromosome encoding method, named fuzzy coding, is proposed for representing real number parameters in a genetic algorithm. Fuzzy coding provides the value of a parameter on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of degree of membership. Thus, it represents the knowledge associated with each parameter and is an indirect method of encoding compared with alternatives, where the parameters are directly represented in the encoding. Fuzzy coding is described and compared with conventional binary coding, gray coding, and floating-point coding. Two test examples, along with neural identification of a nonlinear pH process from experimental data, are studied. It is shown that fuzzy coding is better than the conventional methods and is effective for parameter optimization in problems where the search space is complicated. Index Terms-Binary coding, floating-point coding, fuzzy coding, genetic algorithm, gray coding, neural networks, nonlinear identification. NOMENCLATURE GA Genetic algorithm. EC Evolutionary computation. LM Local model. RBF Radial basis function. MLP Multilayer perceptron. MSE Mean-square error. Grad Gradient learning method. Binary coding. Gray coding. Real number coding. NM Negative medium. NS Negative small. ZR Zero. PS Positive small. PM Positive medium. Degree of membership function. Lower range of a parameter. Upper range of a parameter. Width of membership functions. Crossover probability. Mutation probability. Value of a parameter from linear membership functions. FS Fuzzy sigmoid distribution. FG Fuzzy Gaussian distribution. FN Fuzzy normal distribution.

In this paper we address visual communications via print-and-scan channels from an information-theoretic point of view as communications with side information that targets quality enhancement of visual data at the output of this type of channels. The solution to this problem addresses important aspects of multimedia data processing and management.

In this paper we address visual communications via print-and-scan channels from an information-theoretic point of view as communications with side information that targets quality enhancement of visual data at the output of this type of channels. The solution to this problem addresses important aspects of multimedia data processing and management.

A novel general ready-to-use bit-error rate (BER) expression for one-dimensional constellations is developed. The BER analysis is performed for bit patterns that form a labeling. The number of patterns for equally spaced M -PAM constellations with different BER is analyzed.

The present work shows the design flow of the BSMILC (Biological Signals and Medical Images Lossless Compressor) IP-Core. This IP-Core is a hardware compressor designed to compress without loss data from biological/medical sources, like physiological signals and medical examinations. During all design flow, since conception until back-end phase, it was used the ipPROCESS, a development process proposed specifically to help the design of intellectual property hardware modules.