Latent Factor Modeling of Perceived Quality for Stereoscopic 3D Video Recommendation

Abstract

Numerous stereoscopic 3D movies are released every single year to movie theaters and they evidently generate large revenues. Despite the notable improvements in stereo capturing and 3D video post-production technologies, stereoscopic artefacts continue to appear even in high-budget films. Existing automatic 3D video quality measurement tools can detect distortions in stereoscopic images and videos, but they fail to determine the viewer’s subjective perception of those arte-facts, and how these distortions affect their choices and the overall visual experience. In this paper, we introduce a novel recommendation system for stereoscopic 3D movies based on a latent factor model that meticulously analyzes the viewer’s subjective ratings and the influence of 3D video distortions on their personal preferences. To the best knowledge of the authors, this is definitely a first-of-its-kind model that recommends 3D movies based on quality ratings. It takes the correlation between the viewer’s visual discomfort and the perception of stereoscopic artefacts into account. The proposed model is trained and tested on the benchmark Nama3ds1-cospad1 and LFOVIAS3DPh2 S3D video quality assessment datasets. The experiments highlight the practical efficiency and considerable performance of the resulting matrix-factorization-based recommendation system.