From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
Research thrust(s): Learning with Dynamic Data and Video
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).
LIVE-FB Large-Scale Social Picture Quality Database
The LIVE-FB Large-Scale Social Picture Quality Database includes 39,810 images and 119,430 patches extracted from them, on which we collected about 4M quality scores in total from 7,865 unique subjects.
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