Image coding with wavelet representations, edge information and visual masking

The wavelet transform provides a multiresolution representation of images. Edges, which are visually important, produce large coeecients across several scales in the wavelet transform domain. By tracking and predicting these edge coeecients across scales in the wavelet transform domain, we can greatly improve the compressed image quality with little degradation in compression ratio. This paper proposes a novel model-based edge tracking and prediction in wavelet domain. It separates textures from edges and codes them diierently. Edges are coded via an edge tracking and prediction, while textures are coded with either ordinary wavelet based image coding techniques or a \wavelet-like" lter bank which is similar to the tuning channels in human vision system. The coding noise is then coded with a noise modelling. Visual masking models are also used to ensure the compressed image has little or almost no perceptual distortion.