Compressed Image Restoration via External-Image Assisted Band Adaptive PCA Model Learning

Visually annoying compression artifacts frequently appear in block-based transform coding at low bit rates, due to coarse and independent quantization of transform coefficients in coding blocks. This paper presents a subband adaptive modeling framework for reducing quantization artifacts. In this framework, each patch is jointly regularized by bandwise distribution priors adaptively learned in its PCA transform domain together with a quantization constraint prior in the DCT domain. Since the compression artifacts influence the covariance statistics of coded image patches remarkably, external images are utilized to provide more robust PCA domains for patch sparse modeling. Instead of using a global distribution model for all patches, the distribution prior of each patch is adaptively learned from similar patches within the compressed image itself to address the non-stationarity of image signals. The coefficients in different PCA bands are regularized unequally according to the learned priors. Experimental results show that the proposed scheme outperforms existing schemes in terms of both the objective and the perceptual qualities.