Depth Perception Assessment for Stereoscopic 3D Displays Using Layered Random Dot Stereogram

Abstract

Determining the features of human binocular vision according to given disparity information has gained significant interest with regard to 3-D-relevant fields. However, subjectively distinguishing the features is internally ambiguous owing to personal factors and external susceptibility due to viewing environmental factors. Moreover, high cost and low reproducibility of the subjective assessment method restricts its usability in numerous practical applications. To alleviate these problems, this paper presents a novel visual stimulus, namely layered random dot stereogram (LRDS), and hierarchical paradigm for vote ratings. Based on the guide depth information in LRDS, viewers could recognize considerably obvious feature points, and our two-step rating strategy enhanced the reliability and efficiency of the viewing tests. The experiments prove that our approach successfully reflects the effects of not only personal factors but also display factors on depth perception. Our approach would be beneficial to the 3-D fields of human factor, viewer-interactive systems, and product performance evaluation.