The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Image coding using wavelet transform
Abstract
A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission.
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Citations
Cites
Handbook of Mathematical Functions
McQuarrie, D. A. (1966) · American Journal of Physics · doi:10.1119/1.1972842 · Cited by 40,433
An Algorithm for Vector Quantizer Design
Linde, Y., Buzo, A., & Gray, R. (1980) · IEEE Transactions on Communications · doi:10.1109/tcom.1980.1094577 · Cited by 7,201
The wavelet transform, time-frequency localization and signal analysis
Daubechies, I. (1990) · IEEE Transactions on Information Theory · doi:10.1109/18.57199 · Cited by 6,483
Self-citationThe Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Cited by
Gray, R. M., & Neuhoff, D. L. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.720541 · Cited by 1,642
Modern Image Quality Assessment
Wang, Z., & Bovik, A. C. (2006) · Synthesis Lectures on Image, Video, and Multimedia Processing · doi:10.1007/978-3-031-02238-8 · Cited by 967
Objective video quality assessment
(2003) · Handbook of Video Databases · doi:10.1201/9780203489864-45 · Cited by 359
Indonesian Journal of Electrical Engineering and Computer Science
(2025) · doi:10.11591/ijeecs · Cited by 317
Perceptual Video Processing: Seeing the Future [Point of View]
Bovik, A. C. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2068371 · Cited by 19
Metadata
- Authors
- Antonini, M., Barlaud, M., Mathieu, P., & Daubechies, I.
- Venue
- IEEE Transactions on Image Processing
- Published
- 1992-04
- Volume
- 1
- Issue
- 2
- Pages
- 205-220
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2148593155
- Cited by
- 3,525
- References
- 4
- FWCI
- 64.69
- Type
- journal article
- Language
- en
- Open access
- closed
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- Antonini, M., Barlaud, M., Mathieu, P., & Daubechies, I. (1992). Image coding using wavelet transform. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.136597
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
