TADAC

We have developed a systematic method for constructing large text annotated image databases designed for exploiting vision-language modeling for image quality assessment and present the Text Annotated Distortion, Appearance and Content (TADAC) database containing over 1.6 million images annotated with texts about their semantic contents, distortion characteristics and appearance properties. We used existing labels or automatic image captioning to annotate the semantic content, designed a list of suitable textual phrases for describing the distortion characteristics, and developed automatic algorithms for computing the appearance properties and annotated these properties with suitable textual descriptions. The TADAC database is the first of its kind that is annotated with all three types of quality relevant texts to enable the learning of high level knowledge about all possible factors affecting image quality. TADAC has enabled the development of the first BIQA model (SLIQUE) that jointly models semantic content, distortion and appearance. We will make TADAC publicly available.