Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
Abstract
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" xmlns:xlink="http://www.w3.org/1999/xlink">http://live.ece.utexas.edu/research/quality/DIIVINE_release.zip for public use and evaluation.
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- Authors
- Moorthy, A. K., & Bovik, A. C.
- Affiliations
- The University of Texas at Austin (US)
- ORCID
- Bovik, A. C.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2011-12
- Volume
- 20
- Issue
- 12
- Pages
- 3350-3364
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2129644086
- Semantic Scholar
- 0f77793a43e795402afdbacd3f0aa1d89880a937
- Cited by
- 1,763
- References
- 66
- Citations by year
- 2026: 71 · 2025: 112 · 2024: 129 · 2023: 123 · 2022: 148 · 2021: 139 · 2020: 143 · 2019: 156 · 2018: 179 · 2017: 156 · 2016: 133 · 2015: 90 · 2014: 95 · 2013: 55 · 2012: 22
- FWCI
- 61.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (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 › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- no-reference image quality assessment, natural scene statistics, perceptual image quality, image distortion, full-reference image quality assessment, structural similarity index, PSNR
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