No-Reference Light Field Image Quality Assessment Based on Spatial-Angular Measurement
Not on the map. Why?
Abstract (arXiv)
Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely consider intrinsic factors affecting the LFI quality. In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency. We first measure the spatial quality deterioration by capturing the naturalness distribution of the light field cyclopean image array, which is formed when human observes the LFI. Then, as a transformed representation of LFI, the Epipolar Plane Image (EPI) contains the slopes of lines and involves the angular information. Therefore, EPI is utilized to extract the global and local features from LFI to measure angular consistency degradation. Specifically, the distribution of gradient direction map of EPI is proposed to measure the global angular consistency distortion in the LFI. We further propose the weighted local binary pattern to capture the characteristics of local angular consistency degradation. Extensive experimental results on four publicly available LFI quality datasets demonstrate that the proposed method outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Aug 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 2.
7 papers.
- 2021No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalnesscited by 20
- 2022Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resamplingcited by 7
- 2025Perceptual Visual Quality Assessment: Principles, Methods, and Future Directionscited by 5
- 2024Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutionscited by 3
- 2025NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without Referencescited by 3
- 2023LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Spacecited by 2
- 2024Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Imagescited by 2
- 2025Perceptual Visual Quality Assessment: Principles, Methods, and Future Directions1 Mar 2025
- 2025NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without References11 Jan 2025
- 2024Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions10 Dec 2024
- 2024Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Images19 Aug 2024
- 2023LFACon: Introducing Anglewise Attention to No-Reference Quality Assessment in Light Field Space20 Mar 2023
- 2022Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling31 Aug 2022
- 2021No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness22 Feb 2021