Feedback is Needed for Retakes: An Explainable Poor Image Notification Framework for the Visually Impaired

TL;DR

This paper presents an explainable image quality assessment framework that notifies visually impaired users of flaws and guides retakes, improving captioning quality by filtering low-quality images.

Contribution

It introduces a novel framework that detects low-quality images, explains flaws, and iteratively improves captioning for visually impaired users.

Findings

Improved captioning metrics after filtering low-quality images.

Effective explanation of image flaws for user notification.

Enhanced image captioning performance with high-quality images.

Abstract

We propose a simple yet effective image captioning framework that can determine the quality of an image and notify the user of the reasons for any flaws in the image. Our framework first determines the quality of images and then generates captions using only those images that are determined to be of high quality. The user is notified by the flaws feature to retake if image quality is low, and this cycle is repeated until the input image is deemed to be of high quality. As a component of the framework, we trained and evaluated a low-quality image detection model that simultaneously learns difficulty in recognizing images and individual flaws, and we demonstrated that our proposal can explain the reasons for flaws with a sufficient score. We also evaluated a dataset with low-quality images removed by our framework and found improved values for all four common metrics (e.g., BLEU-4, METEOR, ROUGE-L, CIDEr), confirming an improvement in general-purpose image captioning capability. Our framework would assist the visually impaired, who have difficulty judging image quality.