EDDMF: An Efficient Deep Discrepancy Measuring Framework for Full-Reference Light Field Image Quality Assessment
Zhengyu Zhang, Shishun Tian, Wenbin Zou, Luce Morin, Lu Zhang
Abstract
The increasing demand for immersive experience has greatly promoted the quality assessment research of Light Field Image (LFI). In this paper, we propose an efficient deep discrepancy measuring framework for full-reference light field image quality assessment. The main idea of the proposed framework is to efficiently evaluate the quality degradation of distorted LFIs by measuring the discrepancy between reference and distorted LFI patches. Firstly, a patch generation module is proposed to extract spatio-angular patches and sub-aperture patches from LFIs, which greatly reduces the computational cost. Then, we design a hierarchical discrepancy network based on convolutional neural networks to extract the hierarchical discrepancy features between reference and distorted spatio-angular patches. Besides, the local discrepancy features between reference and distorted sub-aperture patches are extracted as complementary features. After that, the angular-dominant hierarchical discrepancy features and the spatial-dominant local discrepancy features are combined to evaluate the patch quality. Finally, the quality of all patches is pooled to obtain the overall quality of distorted LFIs. To the best of our knowledge, the proposed framework is the first patch-based full-reference light field image quality assessment metric based on deep-learning technology. Experimental results on four representative LFI datasets show that our proposed framework achieves superior performance as well as lower computational complexity compared to other state-of-the-art metrics.