A prediction backed model for quality assessment of screen content and 3-D synthesized images

• Main Authors: Jakhetiya, Vinit, Gu, Ke, Lin, Weisi, Li, Qiaohong, Jaiswal, Sunil Prasad
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Distortion Categorization; Human Vision
• Online Access: https://hdl.handle.net/10356/140048
• Institution: Nanyang Technological University

In this paper, we address problems associated with free-energy-principle-based image quality assessment (IQA) algorithms for objectively assessing the quality of Screen Content (SC) and three-dimensional (3-D) synthesized images and also propose a very fast and efficient IQA algorithm to address these issues. These algorithms separate an image into predicted and disorder residual parts and assume disorder residual part does not contribute much to the overall perceptual quality. These algorithms fail for quality estimation of SC images as information of textual regions in SC images are largely separated into the disorder residual part and less information in the predicted part and subsequently, given a negligible emphasis. However, this is in contrast with the characteristics of human vision. Since our eyes are well trained to detect text in daily life. So, our human vision has prior information about text regions and can sense small distortions in these regions. In this paper, we proposed a new reduced-reference IQA algorithm for SC images based upon a more perceptually relevant prediction model and distortion categorization, which overcomes problems with existing free-energy-principle-based predictors. From experiments, it is validated that the proposed model has a better capability of efficiently estimating the quality of SC images as compared to the recently developed reduced-reference IQA algorithms. We also applied the proposed algorithm to judge the quality of 3-D synthesized images and observed that it even achieves better performance than the full-reference IQA metrics specifically designed for the 3-D synthesized views.