Perceptual Quality Assessment of Omnidirectional Images
arXiv:2207.02674 · doi:10.1109/ISCAS.2018.8351786
Abstract
Omnidirectional images and videos can provide immersive experience of real-world scenes in Virtual Reality (VR) environment. We present a perceptual omnidirectional image quality assessment (IQA) study in this paper since it is extremely important to provide a good quality of experience under the VR environment. We first establish an omnidirectional IQA (OIQA) database, which includes 16 source images and 320 distorted images degraded by 4 commonly encountered distortion types, namely JPEG compression, JPEG2000 compression, Gaussian blur and Gaussian noise. Then a subjective quality evaluation study is conducted on the OIQA database in the VR environment. Considering that humans can only see a part of the scene at one movement in the VR environment, visual attention becomes extremely important. Thus we also track head and eye movement data during the quality rating experiments. The original and distorted omnidirectional images, subjective quality ratings, and the head and eye movement data together constitute the OIQA database. State-of-the-art full-reference (FR) IQA measures are tested on the OIQA database, and some new observations different from traditional IQA are made.
References in corpus (1)
Cited by in corpus (10)
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- Confusing Image Quality Assessment: Towards Better Augmented Reality Experience
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- Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks
- No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness
- State-of-the-art in 360° Video/Image Processing: Perception, Assessment and Compression
- Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory
- Image Quality Assessment for Omnidirectional Cross-reference Stitching
- Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos