No-reference quality assessment using natural scene statistics: JPEG2000

Abstract—Measurement of image or video quality is crucial for many image-processing algorithms, such as acquisition, compres-sion, restoration, enhancement, and reproduction. Traditionally, image quality assessment (QA) algorithms interpret image quality as similarity with a “reference ” or “perfect ” image. The obvious limitation of this approach is that the reference image or video may not be available to the QA algorithm. The field of blind, or no-ref-erence, QA, in which image quality is predicted without the refer-ence image or video, has been largely unexplored, with algorithms focussing mostly on measuring the blocking artifacts. Emerging image and video compression technologies can avoid the dreaded blocking artifact by using various mechanisms, but they introduce other types of distortions, specifically blurring and ringing. In this paper, we propose to use natural scene statistics (NSS) to blindly measure the quality of images compressed by JPEG2000 (or any other wavelet based) image coder. We claim that natural scenes contain nonlinear dependencies that are disturbed by the compres-sion process, and that this disturbance can be quantified and re-lated to human perceptions of quality. We train and test our al-gorithm with data from human subjects, and show that reason-ably comprehensive NSS models can help us in making blind, but accurate, predictions of quality. Our algorithm performs close to the limit imposed on useful prediction by the variability between human subjects. Index Terms—Blind quality assessment (QA), image QA, JPEG2000, natural scene statistics (NSS), no reference (NR) image QA. I