Paired comparison-based subjective quality assessment of stereoscopic images

As 3D image and video content has gained significant popularity, subjective 3D quality assessment has become an important issue for the creation, processing, and distribution of high quality 3D content. Reliable subjective quality assessment of 3D content is often difficult due to the subjects' limited 3D experience, the interaction of multiple quality factors, minor quality differences between stimuli, etc. Among subjective evaluation methodologies, paired comparison has the advantage of improved simplicity and reliability, which can be useful to tackle the aforementioned difficulties. In this paper, we propose a new method to analyze the results of paired comparison-based subjective tests. We assume that ties convey information about the significance of quality score differences between two stimuli. Then, a maximum likelihood estimation is performed to obtain confidence intervals providing intuitive measures of significance of the quality differences. We describe the complete test procedure using the proposed method, from subjective experiment design to outlier detection and score analysis for 3D image quality assessment. Especially, we design the test procedure in a way that quality comparison across different contents is enabled while the number of pair-wise comparisons is minimized. Experimental results on a stereoscopic image database with varying camera distances demonstrate the usefulness of the proposed method and enhanced quality discriminability of paired comparison in comparison to the conventional single stimulus methodology.