From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality

Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved problem of great consequence to the social and streaming media industries that impacts billions of viewers daily. Unfortunately, popular NR prediction models perform poorly on real-world distorted pictures. To advance progress on this problem, we introduce the largest (by far) subjective picture quality database, containing about 40000 real-world distorted pictures and 120000 patches, on which we collected about 4M human judgments of picture quality. Using these picture and patch quality labels, we built deep region-based architectures that learn to produce state-of-the-art global picture quality predictions as well as useful local picture quality maps. Our innovations include picture quality prediction architectures that produce global-to-local inferences as well as local-to-global inferences (via feedback).

PDF Abstract CVPR 2020 PDF CVPR 2020 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Image Quality Assessment MSU NR VQA Database PaQ-2-PiQ SRCC 0.8705 # 7
PLCC 0.8549 # 8
KLCC 0.7079 # 7
Video Quality Assessment MSU NR VQA Database PaQ-2-PiQ SRCC 0.8705 # 13
PLCC 0.8549 # 16
KLCC 0.7079 # 12
Type NR # 1


No methods listed for this paper. Add relevant methods here