A new standardized method for objectively measuring video quality

By Margaret H. Pinson, S. Wolf. IEEE transactions on broadcasting, 2004

1,302 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/974e4f1d0614f2d80bee2a8d9b97c64492d1bd8f

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Citations in the corpus

Massive Online Crowdsourced Study of Subjective and Objective Picture Quality (2015, methodology): “Study framework: We adopted a single stimulus continuous procedure [42] to obtain quality ratings on images where subjects reported their quality judgments by dragging the slider located below the image on the rating interface.”
Study of Subjective and Objective Quality Assessment of Video (2010, methodology): “NTIA [38]) affirm both of these algorithms as extremely prac-”
The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics (2008, background): “Pinson and Wolf’s VQM video quality metric [23] divides sequences into”
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain (2012, background): “This kind of “percentile pooling” has been observed to result in improved correlations with subjective perception of quality [23], [45].”
Blind Prediction of Natural Video Quality (2014, methodology): “SSIM [1], MS-SSIM [2], VSNR [3], MAD [4], and the VIF index [5] are examples of successful FR-IQA algorithms.”
Visual Importance Pooling for Image Quality Assessment (2009, methodology): “We note that in [30] a similar form of pooling is used for video quality assessment, where only lowest 5% of the spatial scores are pooled together.”
Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies (2012, background): “Significant effort has also been applied to designing objective algorithms that are capable of predicting visual quality with high correlation against subjective perception [11]–[14].”
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality (2011, background): “The set of features is the lowest 5% [50], [51] of the structural correlation values so obtained for each pair, leading to a total of fea-tures— .”
Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing (2013, background): “Other FR VQA algorithms account for temporal distortions from temporal filter responses [11], temporal decorrelation [12], evaluating quality over temporal trajectories [13] and studying the temporal evolution of spatial distortions [14].”
A reduced-reference perceptual image and video quality metric based on edge preservation (2012, methodology): “Objective quality assessment methods based on subjective measurements are based either on a perceptual model of the human visual system (HVS) [1], or on a combination of relevant parameters tuned with subjective tests [2,3].”
A Completely Blind Video Integrity Oracle (2016, methodology): “We next compared VIIDEO with five FR indices: MSE, MS-SSIM [45], MOVIE [46], VQM [47], ST-MAD [48], one RR index: STRRED [3] and one learning based blind index: V-BLIINDS [8].”
Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison (2011, methodology): “(not to be confused with NTIA VQM [58]) was defined in terms of a weighted mean distortion and a maximum distortion as follows:”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “Objective quality models, such as the video quality metric (VQM) [362], the perceptual evaluation of speech quality (PESQ) metric [386] and the E-model [51] for voice and video services, were proposed to objectively assess service quality by human beings and infermore “fair” and unbiasedMOS.”