VBLFI: VISUALIZATION-BASED BLIND LIGHT FIELD IMAGE QUALITY ASSESSMENT

Light field image (LFI) contains the intensity and direction information of the scene. The huge amount of data and different visualization methods of LFI brings great challenges to LFI processing and its blind LFI quality assessment. This paper analyzes the human visual perception from the LFI’s visualization, and proposes a novel Visualization-based Blind Light Field Image quality assessment (VBLFI) model. With the depth cues from LFI, we present the concept of mean difference image (MDI). MDI can not only reduce redundant information of LFI, but also contain depth and structural information of LFI. LFI’s multi-scale expression with curvelet transform is used to reflect the multi-channel characteristics of human visual system. So, the corresponding natural scene statistical features and energy features are extracted from MDI and sub-aperture images of LFI in curvelet domain to form the feature vector, further used to predict the LFI quality. Compared to the representative 2D image quality assessment models and the state-of-the-art LFIQA models, the proposed VBLFI model has better prediction accuracy and stability in the public LFI databases.