ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images
Xilei Zhu, Liu Yang, Huiyu Duan, Xiongkuo Min, Guangtao Zhai, Patrick, Le Callet

TL;DR
This paper introduces ESIQAnet, a novel perceptual quality assessment model for egocentric spatial images in XR, supported by the first dedicated IQA database, ESIQAD, with extensive experimental validation.
Contribution
The paper presents the first IQA database for egocentric spatial images and a new multi-stage feature fusion model, ESIQAnet, for accurate quality prediction across different XR display modes.
Findings
ESIQAnet outperforms 22 state-of-the-art IQA models.
The ESIQAD database contains 500 images with MOS under three display modes.
The model effectively captures stereoscopic and egocentric image quality features.
Abstract
With the development of eXtended Reality (XR), photo capturing and display technology based on head-mounted displays (HMDs) have experienced significant advancements and gained considerable attention. Egocentric spatial images and videos are emerging as a compelling form of stereoscopic XR content. The assessment for the Quality of Experience (QoE) of XR content is important to ensure a high-quality viewing experience. Different from traditional 2D images, egocentric spatial images present challenges for perceptual quality assessment due to their special shooting, processing methods, and stereoscopic characteristics. However, the corresponding image quality assessment (IQA) research for egocentric spatial images is still lacking. In this paper, we establish the Egocentric Spatial Images Quality Assessment Database (ESIQAD), the first IQA database dedicated for egocentric spatial images…
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Taxonomy
TopicsAdvanced Image Fusion Techniques · Visual Attention and Saliency Detection · Satellite Image Processing and Photogrammetry
MethodsSoftmax · Attention Is All You Need
