Helping Visually Impaired People Take Better Quality Pictures
Maniratnam Mandal, Deepti Ghadiyaram, Danna Gurari, and Alan C. Bovik

TL;DR
This paper introduces a large dataset and a new predictive model to assess and improve the technical quality of photos taken by visually impaired users, focusing on distortions like blur and noise.
Contribution
It presents the LIVE-Meta VI-UGC dataset and a state-of-the-art blind image quality predictor tailored for visually impaired user-generated content.
Findings
Achieved superior prediction accuracy on VI-UGC images.
Developed a prototype feedback system for guiding users.
Significantly outperformed existing quality models.
Abstract
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…
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Taxonomy
TopicsRetinal Imaging and Analysis · Image and Video Quality Assessment · Image Enhancement Techniques
