Latent Factor Modeling of Users Subjective Perception for Stereoscopic 3D Video Recommendation

TL;DR

This paper presents a novel latent factor model for recommending stereoscopic 3D movies by analyzing viewer subjective ratings and the impact of visual discomfort caused by stereoscopic artifacts, improving prediction accuracy.

Contribution

Introduces the first recommendation system for 3D movies that incorporates viewer subjective perception and stereoscopic artifact effects into a latent factor model.

Findings

Model outperforms existing methods in predicting viewer ratings.

Effective in capturing the correlation between discomfort and stereoscopic artifacts.

Demonstrates strong generalization on benchmark datasets.

Abstract

Numerous stereoscopic 3D movies are released every year to theaters and created large revenues. Despite the improvement in stereo capturing and 3D video post-production technology, stereoscopic artifacts which cause viewer discomfort continue to appear even in high-budget films. Existing automatic 3D video quality measurement tools can detect distortions in stereoscopic images or videos, but they fail to consider the viewer's subjective perception of those artifacts, and how these distortions affect their choices. In this paper, we introduce a novel recommendation system for stereoscopic 3D movies based on a latent factor model that meticulously analyse the viewer's subjective ratings and influence of 3D video distortions on their preferences. To the best of our knowledge, this is a first-of-its-kind model that recommends 3D movies based on stereo-film quality ratings accounting correlation between the viewer's visual discomfort and stereoscopic-artifact perception. The proposed model is trained and tested on benchmark Nama3ds1-cospad1 and LFOVIAS3DPh2 S3D video quality assessment datasets. The experiments revealed that resulting matrix-factorization based recommendation system is able to generalize considerably better for the viewer's subjective ratings.