Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation

• Published in: IEEE journal of selected topics in signal processing Vol. 3; no. 2; pp. 202 - 211
• Main Authors: Li, Qiang, Wang, Zhou
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Assessments; Biological system modeling; Data mining; Degradation; Distortion; Divisive normalization; Feature extraction; Image coding; Image databases; Image quality; image quality assessment; Image representation; Mathematical models; perceptual image representation; reduced-reference image quality assessment (RRIQA); Representations; Signal processing algorithms; statistical image modeling; Wavelet coefficients
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2009.2014497

Reduced-reference image quality assessment (RRIQA) methods estimate image quality degradations with partial information about the ldquoperfect-qualityrdquo reference image. In this paper, we propose an RRIQA algorithm based on a divisive normalization image representation. Divisive normalization has been recognized as a successful approach to model the perceptual sensitivity of biological vision. It also provides a useful image representation that significantly improves statistical independence for natural images. By using a Gaussian scale mixture statistical model of image wavelet coefficients, we compute a divisive normalization transformation (DNT) for images and evaluate the quality of a distorted image by comparing a set of reduced-reference statistical features extracted from DNT-domain representations of the reference and distorted images, respectively. This leads to a generic or general-purpose RRIQA method, in which no assumption is made about the types of distortions occurring in the image being evaluated. The proposed algorithm is cross-validated using two publicly-accessible subject-rated image databases (the UT-Austin LIVE database and the Cornell-VCL A57 database) and demonstrates good performance across a wide range of image distortions.