Space-frequency quantization for wavelet image coding
- 1 May 1997
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 6 (5) , 677-693
- https://doi.org/10.1109/83.568925
Abstract
A new class of image coding algorithms coupling standard scalar quantization of frequency coefficients with tree-structured quantization (related to spatial structures) has attracted wide attention because its good performance appears to confirm the promised efficiencies of hierarchical representation. This paper addresses the problem of how spatial quantization modes and standard scalar quantization can be applied in a jointly optimal fashion in an image coder. We consider zerotree quantization (zeroing out tree-structured sets of wavelet coefficients) and the simplest form of scalar quantization (a single common uniform scalar quantizer applied to all nonzeroed coefficients), and we formalize the problem of optimizing their joint application. We develop an image coding algorithm for solving the resulting optimization problem. Despite the basic form of the two quantizers considered, the resulting algorithm demonstrates coding performance that is competitive, often outperforming the very best coding algorithms in the literature.Keywords
This publication has 28 references indexed in Scilit:
- Joint optimization of scalar and tree-structured quantization of wavelet image decompositionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Joint thresholding and quantizer selection for decoder-compatible baseline JPEGPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Marginal analysis prioritization for image compression based on a hierarchical wavelet decompositionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1993
- Wavelets and filter banks: theory and designIEEE Transactions on Signal Processing, 1992
- Adaptive entropy coded subband coding of imagesIEEE Transactions on Image Processing, 1992
- Trellis coded quantization of memoryless and Gauss-Markov sourcesIEEE Transactions on Communications, 1990
- A theory for multiresolution signal decomposition: the wavelet representationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Orthonormal bases of compactly supported waveletsCommunications on Pure and Applied Mathematics, 1988
- Subband coding of images using vector quantizationIEEE Transactions on Communications, 1988
- Quantizing for minimum distortionIEEE Transactions on Information Theory, 1960