Multi-Exposure Fusion Light Field Image Quality Assessment Based on Object Detection

The multi-exposure fusion algorithms can effectively improve the dynamic range of light field imaging, but distortion is inevitably introduced during the process. Therefore, it is crucial to construct an effective quality assessment method for multi-exposure fusion light field images (MEF-LFIs). Aiming at the significant artifact distortion phenomenon of MEF-LFIs and the limitations of the traditional dynamic region extraction methods, the MEF-LFI quality assessment method combined with object detection is proposed. Firstly, an object detection model is utilized to extract dynamic region masks of the original LFI sequences, and the artifact feature fusion module is constructed for artifact distortion. Subsequently, the MEF-LFIs are converted from the RGB color space to the HSV color space to measure color distortion. Then, the multi-stream feature extraction networks with attention mechanism are constructed to extract artifact, spatial and angular distortion features, respectively. Finally, a regression model is constructed to measure the visual quality of MEF-LFIs. Experimental results show that the proposed method outperforms many representative image quality assessment methods and maintains well consistency with the human visual perception.