GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content
- URL: http://arxiv.org/abs/2305.02422v3
- Date: Tue, 29 Aug 2023 22:12:04 GMT
- Title: GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content
- Authors: Yu-Chih Chen, Avinab Saha, Chase Davis, Bo Qiu, Xiaoming Wang, Rahul
Gowda, Ioannis Katsavounidis, Alan C. Bovik
- Abstract summary: We develop a new gaming-specific NR VQA model called the Gaming Video Quality Evaluator (GAMIVAL)
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.
- Score: 30.96557290048384
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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