A tutorial on modern lossy wavelet image compression: foundations of JPEG 2000
Top Cited Papers
- 1 September 2001
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Signal Processing Magazine
- Vol. 18 (5) , 22-35
- https://doi.org/10.1109/79.952803
Abstract
One of the purposes of this article is to give a general audience sufficient background into the details and techniques of wavelet coding to better understand the JPEG 2000 standard. The focus is on the fundamental principles of wavelet coding and not the actual standard itself. Some of the confusing design choices made in wavelet coders are explained. There are two types of filter choices: orthogonal and biorthogonal. Orthogonal filters have the property that there are energy or norm preserving. Nevertheless, modern wavelet coders use biorthogonal filters which do not preserve energy. Reasons for these specific design choices are explained. Another purpose of this article is to compare and contrast "early" wavelet coding with "modern" wavelet coding. This article compares the techniques of the modern wavelet coders to the subband coding techniques so that the reader can appreciate how different modern wavelet coding is from early wavelet coding. It discusses basic properties of the wavelet transform which are pertinent to image compression. It builds on the background material in generic transform coding given, shows that boundary effects motivate the use of biorthogonal wavelets, and introduces the symmetric wavelet transform. Subband coding or "early" wavelet coding method is discussed followed by an explanation of the EZW coding algorithm. Other modern wavelet coders that extend the ideas found in the EZW algorithm are also described.Keywords
This publication has 30 references indexed in Scilit:
- The wavelet/scalar quantization compression standard for digital fingerprint imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A technique for the efficient coding of the upper bands in subband coding of imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Analysis of low bit rate image transform codingIEEE Transactions on Signal Processing, 1998
- Vector quantization of image subbands: a surveyIEEE Transactions on Image Processing, 1996
- Smooth wavelets, transform coding, and Markov-1 processesIEEE Transactions on Signal Processing, 1995
- Singularity detection and processing with waveletsIEEE Transactions on Information Theory, 1992
- Vector Quantization and Signal CompressionPublished by Springer Nature ,1992
- Wavelets and Dilation Equations: A Brief IntroductionSIAM Review, 1989
- Subband coding of imagesIEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
- Multi-dimensional sub-band coding: Some theory and algorithmsSignal Processing, 1984