Title: Multimedia art data optimisation by integrating UO-CRUSH and Q-learning algorithm VR technology

Authors: Hongying Song; Xiaohong Wang

Addresses: Digital Media Department, Hebei Software Institute, Baoding, 071000, China ' Media Arts Department, Baoding Vocational and Technical College, Baoding, 071051, China

Abstract: A resource management framework based on virtual reality (VR) technology is proposed to address the limitations and poor presentation effects of traditional multimedia art data flat creation. It integrates the UO-CRUSH algorithm driven by resource interest and the Q-learning algorithm driven by user interest to optimise the storage and scheduling of multimedia art data. The experimental results show that the proposed model has an average computation time of 426 seconds, completes an average of 752 tasks in the maximum cycle, and has a uniform distribution of resource placement groups (137-153). In the instance verification, the classification accuracy is not less than 90%, the interactive response speed is 172 ms, and the frame rate of the picture reaches 78fps, which combines good immersion and economy. This model effectively improves the optimisation effect of multimedia art data.

Keywords: multimedia art data; MAD; virtual reality; scalable replica hashing algorithm; reinforcement learning algorithm; resource management.

DOI: 10.1504/IJART.2026.155705

International Journal of Arts and Technology, 2026 Vol.16 No.3, pp.243 - 260

Received: 12 May 2025
Accepted: 16 Sep 2025
Published online: 11 Aug 2026 *

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