VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images

By Damon M. Chandler, Sheila S. Hemami. 2007

1,198 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

The Perception-Distortion Tradeoff (2017, background): “Many alternative, more perceptual, distortion measures have been proposed, including SSIM [40], MS-SSIM [42], IFC [36], VIF [35], VSNR [3] and FSIM [47].”
Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison (2011, methodology): “The VSNR metric presented by Chandler and Hemami [73], is essentially a full-reference still-image quality metric but has also shown a promising performance in assessing video quality when applied on a frame-by-frame basis and then averaged.”
Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation (2009, methodology): “To validate the proposed RRIQA algorithm, two publiclyaccessible subject-rated image databases are used, which are the LIVE database [36] developed at Laboratory for Image and Video Engineering at The University of Texas at Austin and the Cornell-VCL A57 database [37] developed at the Visual Communi”
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality (2019, background): “Full-Reference models: Many full-reference (FR) perceptual picture quality predictors, which make comparisons against high-quality reference pictures, are available [9, 10], [27, 28, 29, 30, 31, 32, 33].”
FSIM: A Feature Similarity Index for Image Quality Assessment (2011, methodology): “Such kinds of models emphasize the importance of HVS’ sensitivity to different visual signals, such as the luminance, the contrast, the frequency content, and the interaction between different signal components [2-4].”
Image Quality Assessment by Comparing CNN Features between Images (2016, background): “Many different approaches for measuring image quality have been proposed, including structural similarity [51, 56], color difference [15], spatial extensions of color difference formulae [22, 47, 54, 58], simulation of detail visibility [38, 34], scene statistics [44], low- and mid-level visual prop”
Study of Subjective and Objective Quality Assessment of Video (2010, background): “The MOVIE index is statistically superior to PSNR, SSIM, Speed SSIM, VSNR and V-VIF on the entire LIVE Video Quality Database.”
Learning to generate images with perceptual similarity metrics (2015, background): “Alternative engineering-based metrics are the visual information ﬁdelity metric [27], which is an information theory-based measure, and the visual signal-to-noise ratio [3].”
Blind Prediction of Natural Video Quality (2014, background): “FR-IQA models now exist that achieve excellent levels of performance, as demonstrated by high correlations with human subjective judgments of visual quality.”
F Mean Squared Error: Love It or Leave It? (2009, methodology): “An interesting recent algorithm called the visual signal-to-noise ratio (VSNR) [20] first determines whether the distortions are below the threshold of visual detection, and then quantifies the distortions that are beyond the threshold in a separate stage.”
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain (2012, methodology): “SSIM [1], MS-SSIM [2], VSNR [3], VIF index [4], and the divisive normalization-based indices in [5] and [6] are examples of successful FR-IQA algorithms.”
Perceptual visual quality metrics: A survey (2011, methodology): “VSNR An image quality metric [17] Section 2.”
From QoS to QoE: A Tutorial on Video Quality Assessment (2015, background): “This coarse-to-ﬁne-grained process is known as Global precedence, one important feature of the HVS [46].”
Unsupervised feature learning framework for no-reference image quality assessment (2012, background): “Examples of state-of-the-art FR-IQA algorithms include VSNR [2], VIF [19], SSIM [25] and MSSIM [27].”
Image Quality Assessment Based on Gradient Similarity (2012, neutral): “Likewise, VSNR is the best for A57 but is relatively poor on TID database.”
Color image database TID2013: Peculiarities and preliminary results (2013, methodology): “This list includes the metric FSIM [7], both component-wise and color versions (the latter is denoted as FSIMc), MSSIM [29], NQM [30], SSIM [31], VIFP [32], VSNR [33], and WSNR [34], all calculated for intensity component.”