Vector Quantization and Signal Compression The Springer International Series in Engineering and Computer Science

This book is devoted to the theory and practice of signal compression; i.e. data compression applied to signals such as speech, audio, images and video signals. The emphasis is on the conversion of analog waveforms into efficient digital representations and on the compression of digital information into the fewest possible bits.

Both operations should yield the highest possible reconstruction fidelity subject to constraints on the bit rate and implementation complexity. The conversion of signals into such efficient digital representations has several goals: to minimize the communication capacity required for transmission of high quality signals such as speech and images or, equivalently, to obtain the best possible fidelity over an available communication channel; to minimize the storage capacity required for saving such information in fast storage media and in archival data bases or, equivalently, to obtain the best possible quality for the largest amount of information stored in a given medium; to provide the simplest possible accurate descriptions of a signal so as to minimize the subsequent complexity of signal processing algorithms such as classification, transformation and encryption.

In addition to these common goals of communication, storage, and signal processing systems, efficient coding of both analog and digital information is intimately connected to a variety of other fields such as pattern recognition, image classification, speech recognition, cluster analysis regression, and decision tree design.

Thus techniques from each field can often be extended to another and combined signal processing operations can take advantage of the similar algorithm structures and designs. This book has been written for use in a graduate course on quantization and signal compression and represents the most comprehensive reference available for research scientists and practitioners working in fields related to the topic.