Perceptual stereoscopic images quality assessment method based on visual adaptable characteristics
Zhi Zheng, Yun Liu, Yun Liu · Optical Engineering · 2019
The quality assessment of stereoscopic images plays an important role in various three-dimensional (3-D) areas and encounters more problems than two-dimensional (2-D) image quality assessment. We propose a perceptual full-reference quality assessment model by considering human adaptable double visual channel. Human binocular combination characteristics and depth perception are both considered in this model, and an adaptable gain-control model is adopted to assign appropriate weights to the information of human visual channels. Experimental results indicate that the proposed algorithm can serve as an efficient predictive image quality feature, which delivers not only highly competitive prediction accuracy but also moderate computational complexity.