Image adaptive multistage vector Quantization scheme for different Multiscale and multidirectional Image representations

Abstract

Digital image and image sequences in uncompressed formats newlinerequire excessive storage capacity and huge transmission bandwidth newlineTherefore compression of digital image and image sequence is necessary for newlineefficient storage and transmission Image compression is possible due to newlineredundancy and irrelevancy Redundancy is a characteristic which is related to newlinepredictability smoothness etc in the image data All natural images exhibit newlinesome form of internal structures which can be explained in terms of statistical newlinedependencies between pixels This dependency is utilized by signal newlinedecomposition The irrelevancy relies on the fact that the human eye cannot newlineperceive certain image degradations due to the visual perception mechanisms newlineThis is utilized by the quantization scheme Thus the role of the signal newlinedecomposition is to decorrelate the signal and make the subsequent newlinequantization process easier Once the decomposed signal samples are newlinequantized an appropriate symbol coding strategy is used to represent the newlinequantized symbols by bits Image compression can be achieved either by newlinemeans of spatial domain techniques or by means of frequency domain newlinetechniques Vector quantization VQ is a quantization technique applied to newlinean ordered set of symbols The superiority of VQ lies in block coding gain newlineflexibility in partitioning the vector space and the ability to exploit intravector newlinecorrelations When used for image compression codebooks are newlinedesigned from training data using some variant of the Generalized Lloyd newlineAlgorithm GLA newline newline