REPID: Rendering Evaluation of Photographic Image Dataset REPID (officially introduced as the Rendering Evaluation of Photographic Image Dataset) is a large scale benchmark designed for Image Rendering Quality Assessment (IRQA) . Unlike traditional Image Quality Assessment (IQA) which focuses on technical degradations like noise or blur, REPID aims to model subjective human aesthetic preferences for different rendering styles of the same scene. Dataset Overview Built upon the MIT Adobe FiveK dataset, REPID provides a massive collection of pairwise human preference annotations for professional and automated renderings. Scenes : 5,000 high resolution RAW photographs. Total Images : 30,000 unique renderings (6 per scene). Total Votes : Over 2.5 million unique votes collected via crowdsourcing. Annotators : 13,648 unique evaluators, with each image pair receiving at least 25 individual votes. Comparison Task : For each of the 15 possible pairs per scene, evaluators indicated "Left preferable", "Right preferable", or "Both equal". Each pair of renderings for the same scene was evaluated by 25 human annotators who indicated which version they preferred or if they considered them equally…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy