Toward a Quality Predictor for Stereoscopic Images via Analysis of Human Binocular Visual Perception
| Autor: | Zhizhuo Zhen, Yun Liu, Fanhui Kong |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Visual perception
General Computer Science Image quality Computer science media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Binocular combination Stereoscopy 02 engineering and technology law.invention law Perception stereoscopic images 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision depth perception ComputingMethodologies_COMPUTERGRAPHICS media_common business.industry General Engineering 020206 networking & telecommunications Cyclopean image Human visual system model 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence Depth perception business lcsh:TK1-9971 cyclopean image |
| Zdroj: | IEEE Access, Vol 7, Pp 69283-69291 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2919155 |
| Popis: | Perceptual stereoscopic image quality assessment (SIQA) has become a challenge research problem due to the poor understanding of human binocular visual characteristics. For the task of SIQA, an intuitive idea is to develop effective models on the basis of the image content and depth perception. In this paper, we propose a full-reference objective quality evaluator for stereoscopic images by simulating binocular behaviors of the human visual system (HVS): Binocular interaction and depth perception. This model is based on a cyclopean image from a novel binocular combination model as image content quality description and a depth binocular combination model from a depth synthesized procedure as depth perception description. The final quality score of the distorted stereoscopic images is calculated by integrating the above two perception indicators. The experimental results on two stereoscopic image quality databases demonstrate that our proposed metric works efficiently for both symmetric and asymmetric distortions and achieves high consistent alignment with subjective observations. |
| Databáze: | OpenAIRE |
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