Image Wavelet Coding Systems: Part II of Set Partition Coding and Image Wavelet Coding Systems
Foundations and Trends® in
Signal Processing
Volume 2 Issue 3
DOI: 10.1561/2000000014
Image Wavelet Coding Systems: Part II of Set Partition Coding and Image Wavelet Coding Systems
William A. Pearlman
Department of Electrical, Computer and System Engineering, Rensselaer
Polytechnic Institute, Troy, NY 12180-3590, USA, pearlw@ecse.rpi.edu
Amir Said
Hewlett-Packard Laboratories, 1501 Page Mill Road, MS 1203, Palo Alto,
CA 94304, USA, Said@hpl.hp.com
SUGGESTED CITATION:
William A.
Pearlman
and
Amir
Said
(2008)
"Image Wavelet Coding Systems: Part II of Set Partition Coding and Image Wavelet Coding Systems",
Foundations and Trends® in Signal Processing: Vol. 2: No 3, pp 181-246.
http:/dx.doi.org/10.1561/2000000014
Abstract
This monograph describes current-day wavelet transform image coding systems. As in the first part, steps of the algorithms
are explained thoroughly and set apart. An image coding system consists of several stages: transformation, quantization, set
partition or adaptive entropy coding or both, decoding including rate control, inverse transformation, de-quantization, and
optional processing (see Figure 1.6). Wavelet transform systems can provide many desirable properties besides high efficiency, such as scalability in quality,
scalability in resolution, and region-of-interest access to the coded bitstream. These properties are built into the JPEG2000
standard, so its coding will be fully described. Since JPEG2000 codes subblocks of subbands, other methods, such as SBHP (Subband
Block Hierarchical Partitioning) [1] and EZBC (Embedded Zero Block Coder) [2], that code subbands or its subblocks independently are also described. The emphasis in this part is the use of the basic
algorithms presented in the previous part in ways that achieve these desirable bitstream properties. In this vein, we describe
a modification of the tree-based coding in SPIHT (Set Partitioning In Hierarchical Trees) [3], whose output bitstream can be decoded partially corresponding to a designated region of interest and is simultaneously quality
and resolution scalable.
This monograph is extracted and adapted from the forthcoming textbook entitled Digital Signal Compression: Principles and Practice by William A. Pearlman and Amir Said, Cambridge University Press, 2009.