An edge-preserving subband coding model based on non-adaptive and adaptive regularization
In this paper, we introduce two new edge-preserving image compression approaches based on the wavelet transform and iterative constrained least square regularization approach. These approaches treat image reconstructed from lossy image compression as the process of image restoration. They utilize the edge information detected from the source image as a priori knowledge for the subsequent reconstruction. In addition, one of the approaches makes use of the spatial characteristics of wavelet coded images to enhance its restoration performance. In order to compromise the overall bit-rate incurred by the additional edge information, a simple vector quantization scheme is proposed to classify the edge bit-planes pattern into a number of binary codevectors. The experiment showed that the proposed approaches could definitely improve both objective and subjective quality of the reconstructed image by recovering more image details and edges.
subband coding image restoration edge-preserving regularization