Development of digital signal processing algorithms for image and communication applications
Yellapantula, Ramakrishna V.
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Digital signal processing algorithms are being used in many applications such as noise, interference cancellation, speech processing, equalization, and image processing. Equalization algorithms in digital communications and image compression algorithms in image processing gained a lot of importance due to their wide spread use in real life. In this thesis, we present algorithms for digital communications and image processing. Blind equalization is a process in which the equalization of the received signal can be done without the aid of any training sequence. Considerable work has been done in case of symbol space blind equalizers but the results are assumed in case of fractionally spaced blind equalizers. In the first part of the thesis we presented the result for fractionally spaced blind equalizers by extending the results of symbol spaced blind equalizers. Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to statistics of each frequency band and to the characteristics of the human visual system. We investigate subband coding techniques for lossless image compression. Compression with Reversible Embedded Wavelets (CREW) is one of the techniques used to compress the continuous-tone still images using wavelets and pyramidal decomposition. Here, we present a new and different implementation for coding wavelet coefficients which provides even better performance than the CREW coding. We provide implementation results on the JPEG test set of images and compare them with state-of-the-art predictive techniques and other techniques based on subband decomposition.