An action-aware combat model for efficient video compression of massively multiplayer online role-playing games on cloud gaming platforms

Basiri, Sardar, Zhang, Kaiwen et Coulombe, Stéphane. 2021. « An action-aware combat model for efficient video compression of massively multiplayer online role-playing games on cloud gaming platforms ». In IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP) (Tampere, Finland, Oct. 06-08, 2021) Institute of Electrical and Electronics Engineers Inc..
Compte des citations dans Scopus : 1.

Prévisualisation

PDF Coulombe-S-2021-24313.pdf - Version acceptée Licence d'utilisation : Tous les droits réservés aux détenteurs du droit d'auteur. Télécharger (1MB) | Prévisualisation

Résumé

Cloud gaming is a rising new trend for remote video gaming. Players send their commands using a thin-client device to a graphics rendering cloud server and receive a compressed video stream in response. However, video games with complex textures and motions, especially at high resolutions, require a substantial bitrate to deliver good visual quality. When the player’s Internet connection is constrained or fluctuates, the visual quality may be significantly reduced, which negatively impacts the playing experience. In this paper, we present an Action-awaRe COmbat moDEl (ARCODE) for massively multiplayer online role-playing games (MMORPGs) running on cloud gaming platforms to improve compression efficiency. ARCODE captures different action data for different object types in the battle scene and determines the importance of each object relative to the player in each game state, considering the actions at the time. Based on the significance of each object to the player, the model determines how frequently its position should be updated. Reducing the number of motion updates in the scene leads to fewer bits needed to encode the video frames. Our experimental results on various test cases show that, for similar visual quality as that of the traditional approach, ARCODE can reduce the video bitrate from 9% to over 40%.

Type de document:	Compte rendu de conférence
Professeur:	Professeur Zhang, Kaiwen Coulombe, Stéphane
Affiliation:	Génie logiciel et des technologies de l'information, Génie logiciel et des technologies de l'information
Date de dépôt:	29 avr. 2022 20:01
Dernière modification:	25 janv. 2023 16:00
URI:	https://espace2.etsmtl.ca/id/eprint/24313

Actions (Authentification requise)

Dernière vérification avant le dépôt