Latent Factor Modeling of Perceived Quality for Stereoscopic 3D Video Recommendation
Balasubramanyam Appina, Mansi Sharma, Santosh Kumar, Peter A. Kara, Aniko Simon, Mary Guindye
In Proceedings of 2021 International Conference on 3D Immersion (IC3D 2021), Brussels, Belgium
Abstract
Throughout the recent decades, the movie industry has been continuously producing stereoscopic 3D contents on a wide scale of varying budget; while some of them are rather limited financially, others benefit from massive monetary reinforcements. It is also quite apparent how the related technologies have significantly improved over the years, resulting in better and better 3D visuals. However, at the time of this paper, it is still the sad truth that even those movies with virtually limitless financial support suffer from stereoscopic artefacts. Although certain scientific efforts have already addressed the objective determination and adequate classification of such issues, sufficiently integrating perceived quality into these models remains to be an open research question. This paper presents our work to reduce the gap between the proposed objective methodology and the human visual system. We introduce a new type of solution that relies on latent factor modeling. The proposed model is trained and tested on the benchmark Nama3ds1-cospad1 and LFOVIAS3DPh2 stereoscopic 3D video quality assessment datasets. The experiments highlight the practical efficiency and considerable performance of the resulting matrix-factorization-based recommendation system.
IC3D2021VideoLatentS3DRecomm.mp4CR_IC3D_latent_factor.pdfCitation
B. Appina, M. Sharma, S. Kumar, P. A. Kara, A. Simon and M. Guindy, "Latent Factor Modeling of Perceived Quality for Stereoscopic 3D Video Recommendation," 2021 International Conference on 3D Immersion (IC3D), 2021, pp. 1-8, doi: 10.1109/IC3D53758.2021.9687271.