1 research outputs found
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)