Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality

• Published in: IEEE transactions on image processing 2011-12, Vol.20 (12), p.3350-3364
• Main Authors: Moorthy, A. K., Bovik, A. C.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Assessments; Blind quality assessment; Blinds; Detection, estimation, filtering, equalization, prediction; Distortion; Distortion measurement; Exact sciences and technology; Feature extraction; Image edge detection; Image processing; Image quality; Information, signal and communications theory; natural scene statistics; no-reference; Quality assessment; Signal and communications theory; Signal processing; Signal, noise; Statistics; Studies; Telecommunications and information theory; Transform coding; Visualization
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2011.2147325

Our approach to blind image quality assessment (IQA) is based on the hypothesis that natural scenes possess certain statistical properties which are altered in the presence of distortion, rendering them un-natural; and that by characterizing this un-naturalness using scene statistics, one can identify the distortion afflicting the image and perform no-reference (NR) IQA. Based on this theory, we propose an (NR)/blind algorithm-the Distortion Identification-based Image Verity and INtegrity Evaluation (DIIVINE) index-that assesses the quality of a distorted image without need for a reference image. DIIVINE is based on a 2-stage framework involving distortion identification followed by distortion-specific quality assessment. DIIVINE is capable of assessing the quality of a distorted image across multiple distortion categories, as against most NR IQA algorithms that are distortion-specific in nature. DIIVINE is based on natural scene statistics which govern the behavior of natural images. In this paper, we detail the principles underlying DIIVINE, the statistical features extracted and their relevance to perception and thoroughly evaluate the algorithm on the popular LIVE IQA database. Further, we compare the performance of DIIVINE against leading full-reference (FR) IQA algorithms and demonstrate that DIIVINE is statistically superior to the often used measure of peak signal-to-noise ratio (PSNR) and statistically equivalent to the popular structural similarity index (SSIM). A software release of DIIVINE has been made available online: http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation.