Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation
Abstract
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.
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The DNT-extended KL divergence algorithm depends on a number of parameters that need to be trained on databases.
“However, this algorithm depends on a number of parameters that need to be trained on databases.”
From RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012) · cites this paper as [13]
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- Authors
- Li, Q., & Wang, Z.
- Affiliations
- The University of Texas at Arlington (US)
- University of Waterloo (CA)
- ORCID
- Venue
- IEEE Journal of Selected Topics in Signal Processing
- Published
- 2009-04
- Volume
- 3
- Issue
- 2
- Pages
- 202-211
- ISSN
- 1932-4553 (ISSN-L)
- OpenAlex
- W2142731874
- Cited by
- 323
- References
- 36
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 8 · 2023: 8 · 2022: 11 · 2021: 10 · 2020: 19 · 2019: 27 · 2018: 32 · 2017: 25 · 2016: 33 · 2015: 27 · 2014: 34 · 2013: 27 · 2012: 27
- FWCI
- 20.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Selected Topics in Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- reduced-reference image quality assessment, divisive normalization, Gaussian scale mixture model, wavelet coefficients, statistical features, image distortion, perceptual image quality
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