Blind Quality Assessment of Light Field Image Based on Spatio-Angular Textural Variation

Light Field Image Quality Assessment (LF-IQA) is vitally important to facilitate the development of immersive technologies. However, current state-of-the-art LF-IQA metrics still struggle to handle Light Field Image (LFI) with massive data in an efficient manner. To cope with this challenge, we propose a simple yet effective Blind LF-IQA metric based on Spatio-Angular Textural Variation, named SATV-BLiF. Given a distorted LFI, we first apply Local Binary Pattern (LBP) operator to measure the textural variation in the spatial and angular domains respectively. Then the generated spatial and angular textural matrices are merged and further transformed into statistical textural histogram features. Finally, Support Vector Regression (SVR) is employed to construct a nonlinear mapping function between the statistical textural histogram features and the perceptual quality score of the distorted LFI. Experimental results on three representative light field databases show that the proposed metric achieves state-of-the-art quality evaluation performance, while having much lower complexity than the existing No-Reference (NR) LF-IQA metrics. The code of the proposed SATV-BLiF metric is available at https://github.com/ZhengyuZhang96/SATV-BLiF.