Embedded wavelet image compression based on a joint probability model

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 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.