Human-centric image quality assessment for head-mounted display (HMD) devices

Pengfei Li; Biaoli Tao; Ze Yuan; Jiayan Zhuang; Chaohao Wang; Shuang Wang

doi:10.1117/12.3008961

12 March 2024 Human-centric image quality assessment for head-mounted display (HMD) devices

Pengfei Li, Biaoli Tao, Ze Yuan, Jiayan Zhuang, Chaohao Wang, Shuang Wang

Proceedings Volume 12913, Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V; 129131J (2024) https://doi.org/10.1117/12.3008961
Event: SPIE AR | VR | MR, 2024, San Francisco, California, United States

Conference Poster

Abstract

This paper proposes a no-reference image quality evaluation model for accurately assessing the quality of real-world images displayed on head-mounted display (HMD) devices. The proposed model employs a simulation of human visual system, providing a reliable measure of image quality. Initially, an efficient convolutional neural network (CNN), specifically designed for noise characteristics, is utilized to obtain a near-perfectly noise-reduced image. The difference between this image and the target image is then calculated in the linear domain. To emulate the contrast sensitivity and masking effects inherent in the human visual system, we introduce a sophisticated frequency-domain filter model in a uniform color space. The resulting multidimensional data from the filters are aggregated and corrected based on the average brightness. Our model's performance is validated against Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics using the TID2013 dataset, revealing superior correlation coefficients. Human factors experiments further confirm the model's reliability and practicality in real-world scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pengfei Li, Biaoli Tao, Ze Yuan, Jiayan Zhuang, Chaohao Wang, and Shuang Wang "Human-centric image quality assessment for head-mounted display (HMD) devices", Proc. SPIE 12913, Optical Architectures for Displays and Sensing in Augmented, Virtual, and Mixed Reality (AR, VR, MR) V, 129131J (12 March 2024); https://doi.org/10.1117/12.3008961

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