GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content

A MATLAB and Python implementation of GAMIng Video Quality EVALuator (GAMIVAL), which is a new gaming-specific no reference video quality assessment model, proposed in IEEE SPL 2023. GAMIVAL achieves superior performance on the new LIVE-Meta Mobile Cloud Gaming video quality database.

All videos, including training ones and testing ones, have their features (2180 features). The features are extracted first by a two-branch framework, which combines 1156 NSS features with 1024 CNN features. Then a support vector regressor is utilized to learn the feature-to-score mappings. The SVR parameters are optimized via a grid-search on the training set. Take LIVE-Meta-Mobile Cloud Gaming database for example, in the paper, 480 videos were used as training set, and other 120 videos were used as testing set. In application to Meta’s cloud game, we can use 600 videos as training set to gain a regressor as a quality predictor.

Schematic flow diagram of the GAMIVAL model. The top portion depicts the spatial and temporal NSS feature computations. The lower portion shows the CNN feature extraction process following NDNetGaming . All of the features are concatenated and utilized to train an SVR model.

Demos

NSS Feature Extraction

demo_compute_NSS_feats.m

CNN Feature Extraction

$ python demo_compute_CNN_feats.py --dataset_name LIVE-Meta-Gaming

Feature Combination

combineFeature.m

Evaluation of BVQA Model

$ bash run_all_bvqa_regression.sh

or

$ python evaluate_bvqa_features_regression.py

Training a SVR / linear SVR model

$ python train_SVR.py

Predict Quality Score (Testing) via a pretrained SVR / linear SVR model

$ python test_SVR.py

Performance

SRCC / PLCC

Metrics	SRCC	PLCC
NIQE	-0.3900	0.4581
BRISQUE	0.7319	0.7394
TLVQM	0.6553	0.6889
VIDEVAL	0.7621	0.7763
RAPIQUE	0.8740	0.9039
GAME-VQP	0.8709	0.8882
NDNet-Gaming	0.8382	0.8200
VSFA	0.9143	0.9264
GAMIVAL	0.9441	0.9524

Box plots of PLCC, SRCC, and KRCC of evaluated BVQA algorithms on the LIVE-Meta MCG dataset over 1000 splits:

Speed

Speed was evaluated on the feature extraction function in all the algorithms. For GAMIVAL, speed was evaluated on demo_compute_NSS_feats.m and demo_compute_CNN_feats.py functions.

Metrics	Platform	Time(sec)
NIQE	MATLAB	728
BRISQUE	MATLAB	205
TLVQM	MATLAB	588
VIDEVAL	MATLAB	959
RAPIQUE	MATLAB	103
GAME-VQP	MATLAB	2053
NDNet-Gaming	Python, Tensorflow	779
VSFA	Python, Pytorch	2385
GAMIVAL	Python, Tensorflow, MATLAB	201

Scatter plots of SRCC of NR-VQA algorithms versus runtime on 1080p videos:

Citation

If you use this code for your research, please cite the following paper:

Y.-C. Chen, A. Saha, C. Davis, B. Qui, X. Wang, I. Katsavounidis, and A. C. Bovik, “Gamival : Video quality prediction on mobile cloud gaming content,” IEEE Signal Processing Letters, 2023, doi: 10.1109/LSP.2023.3255011.

@ARTICLE{10065464,
  author={Chen, Yu-Chih and Saha, Avinab and Davis, Chase and Qiu, Bo and Wang, Xiaoming and Gowda, Rahul and Katsavounidis, Ioannis and Bovik, Alan C.},
  journal={IEEE Signal Processing Letters}, 
  title={GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/LSP.2023.3255011}}

Contacts

• Yu-Chih Chen ( berriechen@utexas.edu ) -- Graduate student, Dept. of ECE, UT Austin.
• Avinab Saha ( avinab.saha@utexas.edu ) -- Graduate student, Dept. of ECE, UT Austin.
• Alan C. Bovik ( bovik@ece.utexas.edu ) -- Professor, Dept. of ECE, UT Austin