No-reference quality assessment for JPEG2000 compressed images

No-Reference quality assessment is a relatively new topic and has been attracting more and more attention in recent years. Due to the limited understanding of the human vision system, most of the existing methods focus on measuring to what extent the image has been distorted. In this paper, by viewing all edge points in JPEG2000 compressed images as 'distorted' or 'un-distorted', we propose using principal component analysis (PCA) to extract the local feature of a given edge point, which indicates both blurring and ringing. We also propose using the probabilities of the given edge point being 'distorted' and 'un-distorted' to model the local distortion metric, which is straightforward and can be easily applied to any type of local feature. Experimental results demonstrate the effectiveness of our scheme.