Eye-tracking-Based Quality Assessment for Image Interpolation
| Autor: | Jinling Chen, Wu Ludi, Yisang Liu, Tiesong Zhao, Yiwen Xu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Machine vision
business.industry Quality assessment Image quality Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology Feature (computer vision) Region of interest Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Image scaling Eye tracking 020201 artificial intelligence & image processing Saliency map Computer vision Artificial intelligence business Interpolation |
| Zdroj: | VCIP |
| DOI: | 10.1109/vcip.2018.8698641 |
| Popis: | In an Image Quality Assessment (IQA) scenario, the Human Vision System (HVS) always acts as the ultimate receiver and valuator of generated images. As an important feature of HVS, the visual attention data has been demonstrated to be able to effectively improve the performance of existing objective quality metrics. However, this feature has not yet been well explored in the IQA of image interpolation. In this paper, we conduct an eye-tracking test on an interpolated image database and investigate the impact of visual attention on IQA of image interpolation. Two visual attention models, saliency map and Region Of Interest (ROI), are then obtained from the eye-tracking data. We further incorporate these models into non-integer interpolated IQA metric and examine their performances. Experiments show that the introduction of eye-tracking features obviously improves the conventional IQA metric for non-integer image interpolation. |
| Databáze: | OpenAIRE |
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