Visual Perception and Quality of Distorted Stereoscopic 3D Images

Download or read book Visual Perception and Quality of Distorted Stereoscopic 3D Images written by Ming-Jun Chen and published by . This book was released on 2012 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on the investigation of human perception of stereoscopic 3D image quality and the development of automatic stereoscopic 3D image quality assessment frameworks. In order to assess human perception of visual quality, a human study was conducted and interactions between image quality, depth quality, visual comfort, and 3D viewing quality were inferred. The results indicate that the overall 3D viewing quality can be well predicted from only image quality and depth quality. Between image and depth quality, image quality seems to be the main factor that enables accurate prediction of overall 3D viewing quality. Two other human studies were conducted to study the effect of masking on stereoscopic distortions. Binocular suppression was observed in the stereo images which were distorted by blur, JPEG compression, or JPEG2K compression, however, no such suppression was observed for stereo images distorted by white noise. Further, a facilitation effect was also observed against disparity variation for blur and JPEG2K distorted stereo images while no depth masking effect was observed. Based on these results, I proposed an automatic full-reference (FR) 3D quality assessment framework. In this framework, I used Gabor filterbank responses to model stimulus strength and then synthesize a Cyclopean image from a stereo image pair. Because the quality of this synthesized view is similar to that of a Cyclopean image, which the human visual system recreates from the stereoscopic stimuli, performing the task of 3D quality assessment on synthesized views can deliver better performance. I verified the performance of this FR framework on the LIVE 3D Image Quality Database and the results indicate that applying the proposed framework improves the performance of FR 2D quality assessment algorithms when applied to stereo 3D images. Further, I proposed a no-reference (NR) 3D quality assessment (QA) algorithm based on natural scene statistics in both the spatial and the depth domain. Experiments indicate that the proposed NR algorithm outperforms all 2D FR QA algorithms and most 3D FR QA models in predicting 3D quality of stereo images. Finally, a fourth subjective study was conducted to understand depth quality when stereo content is free from visual discomfort. The result suggests that human perception of depth quality is correlated with the content of the stereo image and the stereoacuity function of human visual system.