VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
Abstract
This paper presents an efficient metric for quantifying the visual fidelity of natural images based on near-threshold and suprathreshold properties of human vision. The proposed metric, the visual signal-to-noise ratio (VSNR), operates via a two-stage approach. In the first stage, contrast thresholds for detection of distortions in the presence of natural images are computed via wavelet-based models of visual masking and visual summation in order to determine whether the distortions in the distorted image are visible. If the distortions are below the threshold of detection, the distorted image is deemed to be of perfect visual fidelity (VSNR = infinity) and no further analysis is required. If the distortions are suprathreshold, a second stage is applied which operates based on the low-level visual property of perceived contrast, and the mid-level visual property of global precedence. These two properties are modeled as Euclidean distances in distortion-contrast space of a multiscale wavelet decomposition, and VSNR is computed based on a simple linear sum of these distances. The proposed VSNR metric is generally competitive with current metrics of visual fidelity; it is efficient both in terms of its low computational complexity and in terms of its low memory requirements; and it operates based on physical luminances and visual angle (rather than on digital pixel values and pixel-based dimensions) to accommodate different viewing conditions.
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SSIM, VIF, VSNR and JND are successful full reference algorithms that have been shown to perform well in predicting the quality scores of human subjects.
“The structural similarity index (SSIM) [2], visual information fidelity (VIF) [3], visual signal to noise ratio (VSNR) [4] and just noticeable difference (JND) metrix [5], are examples of successful full reference algorithms which have been shown to perform well in predicting the quality scores of human subjects.”
From RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012) · cites this paper as [4]
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- Authors
- Chandler, D. M., & Hemami, S. S.
- Affiliations
- Oklahoma State University (US)
- Cornell University (US)
- ORCID
- Chandler, D.M.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2007-09
- Volume
- 16
- Issue
- 9
- Pages
- 2284-2298
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2009272644
- Semantic Scholar
- 25efdeaaafa7ac8e3b305d3575fe2d241db1a361
- Cited by
- 1,147
- References
- 75
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 28 · 2023: 26 · 2022: 48 · 2021: 64 · 2020: 53 · 2019: 64 · 2018: 91 · 2017: 92 · 2016: 99 · 2015: 90 · 2014: 106 · 2013: 91 · 2012: 86
- FWCI
- 43.73
- 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 › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- image quality metrics, visual fidelity, human visual system, visual masking, contrast threshold, distortion detection, perceived contrast, global precedence, natural images, low computational complexity
- MeSH terms
- Algorithms, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Visual Perception, Reproducibility of Results, Biomimetics
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