DVQ: A digital video quality metric based on human vision

By Andrew B. Watson, James Hu, J. McGowan. 2001

421 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0a030400c1aba1e0ff58bda16af823cbeb1712b7

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Citations in the corpus

• Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing (2013, methodology): “The MOVIE index [1] uses the idea of motion tuning or the varied sensitivity of the human to motion direction to measure distortions.”
• Perceptual visual quality metrics: A survey (2011, methodology): “The base threshold for DCT-based JND due to the spatial CSF Section 3.3.1 v The object velocity perceived by the retina Section 3.3.1 v o The object velocity in the image Section 3.3.1 v e The eye movement velocity Section 3.3.1 W width of an image Section 3.2.2 (x, y) Pixel location Section 3.1 a }”
• Image Quality Assessment: From Error Measurement to Structural Similarity (2004, methodology): “…methods implement sophisticated channel decompositions that are believed to be closely related to the neural responses in the primary visual cortex [2], [15]–[19], many metrics use simpler transforms such as the discrete cosine transform (DCT) [20], [21] or separable wavelet transforms [22]–[24].”
• Why is image quality assessment so difficult? (2002, background): “In the past three to four decades, a great deal of effort has been made to develop new objective image and video quality measurement approaches which incorporate perceptual quality measures by considering human visual system (HVS) characteristics [1, 2, 3, 4, 5, 6, 7, 8, 9].”
• Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison (2011, methodology): “Subsequently, as an extension of Watson’s DVQ [64], Xiao [65] proposed a modification which made use of the fact that the”
• 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].”
• 41 OBJECTIVE VIDEO QUALITY ASSESSMENT (2003, methodology): “Watson’s digital video quality (DVQ) metric operates in the DCT domain and is therefore more attractive from an implementation point of view [24,57] since the DCT is efficient to implement and most video coding standards are based on the DCT.”
• From QoS to QoE: A Tutorial on Video Quality Assessment (2015, background): “It incorporates spatial and temporal filtering, spatial frequency channels and contrast masking [41], [42].”