Blind Light Field Image Quality Assessment Using Multiplane Texture and Multilevel Wavelet Information

Zhengyu Zhang, Shishun Tian, Jianjun Xiang, Wenbin Zou, Luce Morin, Lu Zhang

Abstract: Light Field Image (LFI) has garnered remarkable interest and fascination due to its burgeoning significance in immersive applications. Although the abundant information in LFIs enables a more immersive experience, it also poses a greater challenge for Light Field Image Quality Assessment (LFIQA), especially when reference information is inaccessible. In this paper, inspired by the holistic visual perception of high-dimensional LFIs and neuroscience studies on the Human Visual System (HVS), we propose a novel Blind Light Field image quality assessment metric by exploring MultiPlane Texture and Multilevel Wavelet Information, abbreviated as MPT-MWI-BLiF. Specifically, considering the texture sensitivity of the secondary visual cortex (V2), we first convert LFIs into multiple individual planes and capture textural variations from these planes. Then, the statistical histogram of textural variations for all planes is calculated as holistic textural variation features. In addition, motivated by the fact that neuronal responses in the visual cortex are frequency-dependent, we simulate this visual perception process by decomposing LFIs into multilevel wavelet subbands with Four-Dimensional Discrete Haar Wavelet Transform (4D-DHWT). After that, the subband geometric features of first-level 4D-DHWT subbands and the coefficient intensity features of second-level 4D-DHWT subbands are computed respectively. Finally, we combine all the extracted quality-aware features and employ the widely-used Support Vector Regression (SVR) to predict the perceptual quality of LFIs. To fully validate the effectiveness of the proposed metric, we perform extensive experiments on five representative LFIQA databases with two cross-validation methods. Experimental results demonstrate the superiority of the proposed metric in quality evaluation, as well as its low time complexity compared to other state-of-the-art metrics. The full code will be publicly available at https://github.com/ZhengyuZhang96/MPT-MWI-BLiF

External IDs:dblp:journals/tbc/ZhangTXZMZ25