ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction

26 Oct 2020  ·  Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli, Alan C. Bovik ·

We consider the problem of conducting frame rate dependent video quality assessment (VQA) on videos of diverse frame rates, including high frame rate (HFR) videos. More generally, we study how perceptual quality is affected by frame rate, and how frame rate and compression combine to affect perceived quality. We devise an objective VQA model called Space-Time GeneRalized Entropic Difference (GREED) which analyzes the statistics of spatial and temporal band-pass video coefficients. A generalized Gaussian distribution (GGD) is used to model band-pass responses, while entropy variations between reference and distorted videos under the GGD model are used to capture video quality variations arising from frame rate changes. The entropic differences are calculated across multiple temporal and spatial subbands, and merged using a learned regressor. We show through extensive experiments that GREED achieves state-of-the-art performance on the LIVE-YT-HFR Database when compared with existing VQA models. The features used in GREED are highly generalizable and obtain competitive performance even on standard, non-HFR VQA databases. The implementation of GREED has been made available online: https://github.com/pavancm/GREED

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Video Quality Assessment LIVE-YT-HFR ST-GREED SRCC 0.8822 # 1
Video Quality Assessment MSU Video Quality Metrics Benchmark ST-GREED SRCC 0.7547 # 14
PLCC 0.8116 # 14
KLCC 0.5905 # 14
Type FR # 1

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