Exploring Rich Subjective Quality Information for Image Quality Assessment in the Wild
Shanghai Jiao Tong University · Peng Cheng Laboratory · +1 more institution
Abstract
Traditional in the wild image quality assessment (IQA) models are generally trained with the quality labels of mean opinion score (MOS), while missing the rich subjective quality information contained in the quality ratings, for example, the standard deviation of opinion scores (SOS) or even distribution of opinion scores (DOS). In this paper, we propose a novel IQA method named RichIQA to explore the rich subjective rating information beyond MOS to predict image quality in the wild. RichIQA is characterized by two key novel designs: 1) a three-stage image quality prediction network which exploits the powerful feature representation capability of the Convolutional vision Transformer (CvT) and mimics the…
Citation impact
- FWCI
- 90.13
- Percentile
- 100%
- References
- 67
Authors
7Topics & keywords
- Quality (philosophy)
- Image quality
- Computer science
- Computer vision
- Artificial intelligence
- Quality assessment
- Image processing
- Image (mathematics)