Blind Quality Evaluator of Light Field Images by Group-Based Representations and Multiple Plane-Oriented Perceptual Characteristics

Due to the emergency of multi-view cameras and commercial Light Field (LF) cameras, the demand of high-performance LF quality evaluator is of great significance for guiding LF acquisition, processing and application and further promoting the visual perceived quality of LF visualizations. However, LF Images (LFIs), as high-dimensional data, suffer from various quality degradations not only in the spatial domain but also in the angular domain. Therefore, it is of great challenge to predict LF quality accurately. An effective LF evaluator should be able to represent these heterogeneous artifacts. In this paper, we provide a novel No-Reference LF Quality Assessment Evaluator (NR LF-QAE) to tackle this problem. Firstly, to measure angular consistency among viewports, we utilize group-based representations to character information similarity of aligned view stacks. Secondly, to better describe the texture information of LFIs, unifying spatial-angular texture statistic measurement is performed via Local Binary Patterns from Three Orthogonal Planes (LBP-TOP). Thirdly, we design 3D Log-Gabor filters to extract LF global structure information in Sub-Aperture Images (SAIs) as spatial feature characterizations and 2D Log-Gabor filters are adopted to characterize ray direction/depth information in Epipolar Plane Images (EPIs) as angular feature characterizations. By comprehensive LF information analyses in angular consistency and spatial-angular feature extraction with texture and structure descriptors, experimental results demonstrate the superiority of the proposed NR LF-QAE over the state-of-the-art comparative models in predicting the quality of LFIs on three available benchmark databases. The code will be released at https://github.com/zerosola/NR-LF-QAE .