Cuid: A new study of perceived image quality and its subjective assessment
This work addresses the need for reliable subjective data to improve IQA algorithms for researchers and developers in computer vision, though it is incremental as it focuses on data collection rather than a new method.
The paper tackles the problem of limited subjective data for image quality assessment (IQA) by conducting a controlled study to collect human perceptual ratings on images with various distortions, resulting in a publicly available database for algorithm calibration and validation.
Research on image quality assessment (IQA) remains limited mainly due to our incomplete knowledge about human visual perception. Existing IQA algorithms have been designed or trained with insufficient subjective data with a small degree of stimulus variability. This has led to challenges for those algorithms to handle complexity and diversity of real-world digital content. Perceptual evidence from human subjects serves as a grounding for the development of advanced IQA algorithms. It is thus critical to acquire reliable subjective data with controlled perception experiments that faithfully reflect human behavioural responses to distortions in visual signals. In this paper, we present a new study of image quality perception where subjective ratings were collected in a controlled lab environment. We investigate how quality perception is affected by a combination of different categories of images and different types and levels of distortions. The database will be made publicly available to facilitate calibration and validation of IQA algorithms.