A Large-scale Evaluation of the bitstream-based video-quality model ITU-T P.1204.3 on Gaming Content

The streaming of gaming content, both passive and interactive, has increased manifolds in recent years. Gaming contents bring with them some peculiarities which are normally not seen in traditional 2D videos, such as the artificial and synthetic nature of contents or repetition of objects in a game. In addition, the perception of gaming content by the user is different from that of traditional 2D videos due to its pecularities and also the fact that users may not often watch such content. Hence, it becomes imperative to evaluate whether the existing video quality models usually designed for traditional 2D videos are applicable to gaming content. In this paper, we evaluate the applicability of the recently standardized bitstream-based video-quality model ITU-T P.1204.3 on gaming content. To analyze the performance of this model, we used 4 different gaming datasets (3 publicly available + 1 internal) not previously used for model training, and compared it with the existing state-of-the-art models. We found that the ITU P.1204.3 model out of the box performs well on these unseen datasets, with an RMSE ranging between 0.38 − 0.45 on the 5-point absolute category rating and Pearson Correlation between 0.85 − 0.93 across all the 4 databases. We further propose a full-HD variant of the P.1204.3 model, since the original model is trained and validated which targets a resolution of 4K/UHD-1. A 50:50 split across all databases is used to train and validate this variant so as to make sure that the proposed model is applicable to various conditions.