Embedded Wavelet Image Compression
Based on a Joint Probability Model
Published in:
Proc. 4th International Conference on Image Processing
Santa Barbara, CA. October 26-29, 1997.
doi: 10.1109/ICIP.1997.647994
© IEEE Signal Processing Society.
Subsequent full-length journal publication:
IEEE Trans. Image Processing, 1999.
We present an embedded image coder based on a statistical
characterization of natural images in the wavelet transform domain.
We describe the joint distribution between pairs of coefficients at
adjacent spatial locations, orientations, and scales. Although the
raw coefficients are nearly uncorrelated, their {\em magnitudes} are
highly correlated. A linear magnitude predictor, coupled with both
multiplicative and additive uncertainties, provides a reasonable
description of the conditional probability densities. We use this
model to construct an image coder called EPWIC (Embedded Predictive
Wavelet Image Coder), in which subband coefficients are encoded one
bit-plane at a time using a non-adaptive arithmetic encoder.
Bit-planes are ordered using a greedy algorithm that considers the MSE
reduction per encoded bit. We demonstrate the quality of the
statistical characterization by comparing rate-distortion curves of
the coder to several standard coders.
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