Describe Quality Issues in Images Taken by People Who Are Blind
Motivated by the aim to tie the assessment of image quality to practical vision tasks, we introduce a new image quality assessment dataset that emerges from a real use case. Our dataset is built around 39,181 images that were taken by people who are blind who were authentically trying to learn about images they took using the VizWiz mobile phone application. Users submitted these images to overcome real visual challenges that they faced in their daily lives. Of these images, 17% were submitted to collect image captions from remote humans. The remaining 83% were submitted with a question to collect answers to their visual questions. For each image, we asked crowdworkers to either supply a caption describing it or clarify that the quality issues are too severe for them to be able to create a caption. We call this task the unrecognizability classification task. We also ask crowdworkers to label each image with perceived quality flaws: blur, overexposure (bright), underexposure (dark), improper framing, obstructions, and rotated views. We call this task the quality flaws classification task. Altogether, we call this dataset VizWiz-QualityIssues. Algorithms for these tasks can be of immediate use to blind photographers.
Dataset, Challenge, & Code
The new dataset is described in the following publication: