This book explains the stages necessary to create a wavelet compression system for images and describes state-of-the-art systems used in image compression standards and current research. It starts with a high level discussion of the properties of the wavelet transform, especially the decomposition into multi-resolution subbands. It continues with an exposition of the null-zone, uniform quantization used in most subband coding systems and the optimal allocation of bitrate to the different subbands. Then the image compression systems of the FBI Fingerprint Compression Standard and the JPEG2000 Standard are described in detail. Following that, the set partitioning coders SPECK and SPIHT, and EZW are explained in detail and compared via a fictitious wavelet transform in actions and number of bits coded in a single pass in the top bit plane. The presentation teaches that, besides producing efficient compression, these coding systems, except for the FBI Standard, are capable of writing bit streams that have attributes of rate scalability, resolution scalability, and random access decoding. Many diagrams and tables accompany the text to aid understanding. The book is generous in pointing out references and resources to help the reader who wishes to expand his knowledge, know the origins of the methods, or find resources for running the various algorithms or building his own coding system. Table of Contents: Introduction / Characteristics of the Wavelet Transform / Generic Wavelet-based Coding Systems / The FBI Fingerprint Image Compression Standard / Set Partition Embedded Block (SPECK) Coding / Tree-based Wavelet Transform Coding Systems / Rate Control for Embedded Block Coders / Conclusion

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William A. Pearlman is currently Professor Emeritus in the Electrical, Computer and Systems Engineering at Rensselaer Polytechnic Institute (RPI). He obtained B.S. and M.S. degrees from MIT in 1963 and his Ph.D. degree from Stanford University in 1974. He joined RPI in 1979 and became Professor in 1988. Prior to joining RPI, he had been a faculty member at the University of Wisconsin-Madison for five years. In addition, he had held industrial positions at Lockheed Missiles and Space Company and GTE-Sylvania and has consulted for several organizations. He has authored or co-authored more than 200 publications in the fields of signal, image and video compression, information theory, communications theory, and digital signal processing. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and of SPIE The International Society for Optical Engineering. At Visual Communications and Image Processing (VCIP) 2010 in Huangshan, China, where he was keynote speaker, he was presented a Certificate of Appreciation in recognition of his leadership and contributions to this conference since its inception in 1986. He received the IEEE Circuits and Systems Society 1998 Video Technology Transactions Best Paper Award and the IEEE Signal Processing Society 1998 Best Paper Award in the Area of Multidimensional Signal and Image Processing. He is co-inventor of two celebrated image compression algorithms, Set Partitioning in Hierarchical Trees (SPIHT) and Set Partitioning Embedded Block (SPECK) coding. In 2001, he received the Robert Bosch Foundation Award in Appreciation of Outstanding Works in the Field of Picture Coding. He has also delivered several keynote or invited plenary lectures on topics in signal compression and is lead author of the recent textbook, Digital Signal Compression: Principles and Practice, with co-author Amir Said, published by Cambridge University Press. He is also founder, president, and chief scientific officer of PrimaComp, Inc., a company that specializes in developing innovative software for signal and image compression.

Inhalt
Introduction.- Characteristics of the Wavelet Transform.- Generic Wavelet-based Coding Systems.- The FBI Fingerprint Image Compression Standard.- Set Partition Embedded Block (SPECK) Coding.- Tree-based Wavelet Transform Coding Systems.- Rate Control for Embedded Block Coders.- Conclusion.