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Cited by 270

The DOI matches its Crossref record.

Statistical models for images: compression, restoration and synthesis

Simoncelli, E.P.·2002·Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)
DOI 10.1109/acssc.1997.680530
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Abstract

We present a parametric statistical model for visual images in the wavelet transform domain. We characterize the joint densities of coefficient magnitudes at adjacent spatial locations, adjacent orientations, and adjacent spatial scales. The model accounts for the statistics of a wide variety of visual images. As a demonstration of this, we used the model to design a progressive image encoder with state-of-the-art rate-distortion performance. We also show promising examples of image restoration and texture synthesis.

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Metadata

Authors
Simoncelli, E. P.
Venue
Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)
Publisher
IEEE Comput. Soc
Published
2002-11-22
Volume
1
Pages
673-678
ISSN
1058-6393 (ISSN-L)
DOI
10.1109/acssc.1997.680530
OpenAlex
W2101349782
Cited by
270
References
0
FWCI
15.69
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Statistical models for images: compression, restoration and synthesis. https://app.cochat.ai/papers/10.1109/acssc.1997.680530
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Simoncelli, E. P. (2002). Statistical models for images: compression, restoration and synthesis. Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136). https://doi.org/10.1109/acssc.1997.680530

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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.