  Perception-based image analysis technologies can be used to help visually
impaired people take better quality pictures by providing automated guidance,
thereby empowering them to interact more confidently on social media. The
photographs taken by visually impaired users often suffer from one or both of
two kinds of quality issues: technical quality (distortions), and semantic
quality, such as framing and aesthetic composition. Here we develop tools to
help them minimize occurrences of common technical distortions, such as blur,
poor exposure, and noise. We do not address the complementary problems of
semantic quality, leaving that aspect for future work. The problem of assessing
and providing actionable feedback on the technical quality of pictures captured
by visually impaired users is hard enough, owing to the severe, commingled
distortions that often occur. To advance progress on the problem of analyzing
and measuring the technical quality of visually impaired user-generated content
(VI-UGC), we built a very large and unique subjective image quality and
distortion dataset. This new perceptual resource, which we call the LIVE-Meta
VI-UGC Database, contains $40$K real-world distorted VI-UGC images and $40$K
patches, on which we recorded $2.7$M human perceptual quality judgments and
$2.7$M distortion labels. Using this psychometric resource we also created an
automatic blind picture quality and distortion predictor that learns
local-to-global spatial quality relationships, achieving state-of-the-art
prediction performance on VI-UGC pictures, significantly outperforming existing
picture quality models on this unique class of distorted picture data. We also
created a prototype feedback system that helps to guide users to mitigate
quality issues and take better quality pictures, by creating a multi-task
learning framework.
