Statistical Models for Images: Compression, Restoration and Synthesis

E P Simoncelli

Published in Proc 31st Asilomar Conf on Signals, Systems and Computers, vol.1 pp. 673--678, Nov 1997.
© IEEE Signal Processing Society
DOI: 10.1109/ACSSC.1997.680530

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    • 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've 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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    Random Cascades on Wavelet Trees and Their Use in Modeling and Analyzing Natural Imagery. M J Wainwright and E P Simoncelli and A S Willsky. Applied and Computational Harmonic Analysis, 11(1), 2001.Random cascades on wavelet trees and their use in modeling and analyzing natural imagery
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  • Compression: Image Compression via Joint Statistical Characterization in the Wavelet Domain. R W Buccigrossi and E P Simoncelli. IEEE Trans Image PRocessing, 8(12):1688-1701, Dec 1999.Image compression via joint statistical characterization in the wavelet domain
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  • Texture Modeling: A parametric texture model based on joint statistics of complex wavelet coefficients. J Portilla and E P Simoncelli. Int'l Journal of Computer Vision 40(1):49-71, October, 2000.A parametric texture model based on joint statistics of complex wavelet coefficients
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