Multi-exposure fusion light field image quality assessment with motion region detection

Keke Yao; Jun Cheng; Yeyao Chen; Yueli Cui; Mei Yu; Gangyi Jiang

doi:10.1117/12.3025865

21 February 2024 Multi-exposure fusion light field image quality assessment with motion region detection

Keke Yao, Jun Cheng, Yeyao Chen, Yueli Cui, Mei Yu, Gangyi Jiang

Proceedings Volume 13080, International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024); 130800D (2024) https://doi.org/10.1117/12.3025865
Event: International Conference on Frontiers of Applied Optics and Computer Engineering, 2024, Kunming, China

Abstract

Compared to ordinary light field image (LFI), multi-exposure fusion light field image (MEF-LFI) can record more visual information and details of scene. However, MEF-LFI also produces distortions while enhancing LFI, leading to quality degradation. Therefore, it is crucial to develop effective MEF-LFI quality assessment models. This paper proposes a multi-exposure fusion light field image quality assessment method with motion region detection, which considers that the artifact distortion of MEF-LFI synthesized from dynamic scenes usually occurs in motion regions. A motion region detection module is designed for detecting artifact distortion in MEF-LFI. Considering that tone mapping (TM) operations can cause texture distortion in MEF-LFI, the spectral texture distortion feature extraction module and the spatial domain gradient feature extraction module are designed by combining Curvelet transform and Scharr operator, respectively. Due to the distortion of color shift in MEF-LFI, the color feature extraction module is constructed with the characteristics of HSI color model. In addition, considering the unique angular distortion of MEF-LFI, the angular feature extraction module is designed with Log-Gabor operator. Finally, the extracted feature vector is input into the support vector regression model to predicate the quality for MEF-LFI. The experimental results show that the proposed method is superior to the representative quality assessment methods and has better consistency with the human visual perception.

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

Citation Download Citation

Keke Yao, Jun Cheng, Yeyao Chen, Yueli Cui, Mei Yu, and Gangyi Jiang "Multi-exposure fusion light field image quality assessment with motion region detection", Proc. SPIE 13080, International Conference on Frontiers of Applied Optics and Computer Engineering (AOCE 2024), 130800D (21 February 2024); https://doi.org/10.1117/12.3025865

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KEYWORDS

Feature extraction

Image quality

Motion detection

Image fusion

Color

RGB color model

Raster graphics

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