The mobile cloud gaming industry has been rapidly growing over the last decade. When streaming gaming videos are transmitted to customers' client devices from cloud servers, algorithms that can monitor distorted video quality without having any reference video available are desirable tools. However, creating No-Reference Video Quality Assessment (NR VQA) models that can accurately predict the quality of streaming gaming videos rendered by computer graphics engines is a challenging problem, since gaming content generally differs statistically from naturalistic videos, often lacks detail, and contains many smooth regions. Until recently, the problem has been further complicated by the lack of adequate subjective quality databases of mobile gaming content. We have created a new gaming-specific NR VQA model called the Gaming Video Quality Evaluator (GAMIVAL), which combines and leverages the advantages of spatial and temporal gaming distorted scene statistics models, a neural noise model, and deep semantic features. Using a support vector regression (SVR) as a regressor, GAMIVAL achieves superior performance on the new LIVE-Meta Mobile Cloud Gaming (LIVE-Meta MCG) video quality database.
@article{arxiv.2305.02422,
title = {GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content},
author = {Yu-Chih Chen and Avinab Saha and Chase Davis and Bo Qiu and Xiaoming Wang and Rahul Gowda and Ioannis Katsavounidis and Alan C. Bovik},
journal= {arXiv preprint arXiv:2305.02422},
year = {2023}
}
Comments
Accepted to IEEE SPL 2023. The implementation of GAMIVAL has been made available online: https://github.com/lskdream/GAMIVAL