Adaptive wavelet filter design for digital signal processing systems



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Texas Tech University


Discrete wavelet transform has been used in many image/signal processing applications in recent years. However, the design of optimized and adaptive wavelet filter banks is still a significant research topic, specifically in image/signal compression. A number of wavelet-based advanced lossy compression algorithms provide high-fidelity reconstmction of input images at computationally intensive costs. The present work investigates the potential and the limitations of optimized adaptive design of two-channel perfect reconstmction filters when the signal in a channel is subjected to coarse quantization during the encoding process of such advanced compression algorithms.

A real-time optimal two-channel perfect reconstmction filter bank design algorithm has been developed and implemented in a digital signal processor. The algorithm has been used in a newly developed execution time reduction method to reduce the computational costs and data storage requirement of image compression algorithms. A reduction of execution time by two to three times has been achieved without adding appreciable distortion to the reconstmcted image.



Signal processing, Adaptive signal processing, Image processing, Image compression, Adaptive filters