Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing

By Rajiv Soundararajan, A. Bovik. IEEE transactions on circuits and systems for video technology (Print), 2013

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Blind Prediction of Natural Video Quality (2014, background): “We have also found that a simple and regular local natural video statistic (NVS) model nicely describes filtered or transformed time-differential (or frame differenced) videos in the wavelet and DCT domains [25], [37].”
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2020, methodology): “RR-VQA models only make use of a limited amount of reference information [24], [25].”
Perceptual quality prediction on authentically distorted images using a bag of features approach (2016, methodology): “There is considerable evidence that local centersurround excitatory-inhibitory processes occur at several types of retinal neurons (Bovik, 2013; Kuffler, 1953), thus providing a bandpass response to the visual signal’s luminance.”
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation (2019, neutral): “ity assessment metric [42].”