Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190737 | 2020 IEEE International Conference on Image Processing (ICIP) |
Keywords | DocType | ISSN |
Image quality assessment,visual perception,subjective testing,mean opinion score,objective metric | Conference | 1522-4880 |
ISBN | Citations | PageRank |
978-1-7281-6395-6 | 1 | 0.35 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucie Lévêque | 1 | 1 | 0.35 |
| Ji Yang | 2 | 1 | 0.35 |
| Xiaohan Yang | 3 | 1 | 0.35 |
| Pengfei Guo | 4 | 2 | 1.70 |
| Kenneth Dasalla | 5 | 1 | 0.35 |
| Leida Li | 6 | 8 | 4.15 |
| Yingying Wu | 7 | 1 | 0.35 |
| Hantao Liu | 8 | 328 | 27.86 |