An adaptive edge model in the wavelet domain for wavelet image coding

State-of-art wavelet coders owe their performance to smart ideas for exploiting inter and intra-band dependencies of wavelet coefficients. We claim that developing more efficient coders requires us to look at the main source of these dependencies; i.e., highly localized information around edges. This paper investigates the structural relationships among wavelet coefficients based on an idealized view of edge behavior, and proposes a simple edge model that explains the roots of existing dependencies. We describe how the model is used to approximate and estimate the significant wavelet coefficients. Simulations support its relevance for understanding and analyzing edge information. Specifically, model-based estimation within the space-frequency quantization (SFQ) framework increases the peak signal-to-noise ratio (PSNR) by up to 0.3 dB over the original SFQ coding algorithm. Despite being simple, the model provides valuable insights into the problem of edge-based adaptive modeling of value and location information in the wavelet domain.