Visual Feature Fusion-based Optimization of VR Stimulus Images Related to Mental Health and Psychological Stress
Ling ZengAbstract
Objective
Unoptimized VR psychological stimulus images produce mismatched visual stimulation, generate extra psychological pressure for participants, disturb stable mental states and reduce the accuracy of mental health assessment. This paper constructs an image optimization scheme based on multi-dimensional visual feature fusion, aiming to standardize psychological stimulus intensity, lower irrelevant visual psychological pressure and guarantee reliable evaluation of subjects’ mental health status.
Subjects and Methods
This study takes participants receiving VR psychological intervention as research objects. It adopts visual feature extraction algorithm, contrast stimulus experiment and psychological scale testing, analyzes the correlation between raw image visual features, induced psychological pressure, fluctuating mental states and mental health test results, and builds a visual feature fusion optimization model.
Results
The visual feature fusion optimization method effectively filters interfering visual information, controls inappropriate stimulus intensity and relieves extra psychological pressure. Optimized VR images maintain mild and controllable stimulation, stabilize subjects’ mental states, eliminate biased interference in detection and significantly improve the objectivity of mental health evaluation.
Conclusions
Visual feature fusion can realize fine adjustment of VR psychological stimulus images. The proposed optimization approach reduces redundant visual psychological pressure, avoids drastic fluctuations of mental states during testing, ensures stable mental health detection environment, and provides technical support for standardized VR-assisted psychological counseling and mental health
Acknowledgement
This work was supported by the Innovation and Development Center for Ideological and Political Work in Colleges and Universities of the Ministry of Education (Zhejiang Shuren University), Research on the Construction and Application of an XR Immersive Teaching Scenario Library for the "Great Ideological and Political Course" in Higher Vocational Colleges from the Perspective of Digitalization of Red Resources (Project Number: ZSSZYB202609); Research Center for Culture and Tourism Collaborative Innovation and Development, Innovative Practice Research on Digital Humans Assisting the Development of Luzhou' s Red Cultural Tourism Industry in the Context of Artificial Intelligence (Project Number: WL2501).
Corresponding Author
Ling Zeng, Luzhou Vocational & Technical School, Luzhou 646000, China.