Automatically optimised stereoscopic camera control based on an assessment of 3D video quality of experience

Abstract

Stereoscopic 3D (S3D) technologies have gained significant attention due to their wide applications. However, producing high-quality S3D content is still a challenging task that requires careful handling to achieve the artistic intent and maintain visual comfort. In this study, we present an automatically controlled stereoscopic camera controller that specifically addresses the challenges in S3D content production. The key idea that distinguishes our method from the existing work is that our method aims to predict the 3D Quality of Experience (QoE) in the production stage so that the optimised camera parameters can be obtained automatically. To this end, considering two interconstraint indicators, i.e., visual comfort and perceived depth, we collect and label a data set of S3D video scene clips and generate a 3D video QoE assessment model that can guide the optimisation of the stereoscopic camera parameters. We describe how to implement our system into a modern production pipeline that has been used in some projects, including commercial ones. The experimental results, including the user studies, demonstrate that our system enhances the perceived depth without creating visual fatigue and that our controller can make the production of S3D content easier and more efficient.