Pseudo Video and Refocused Images-Based Blind Light Field Image Quality Assessment

• Published in: IEEE transactions on circuits and systems for video technology Vol. 31; no. 7; pp. 2575 - 2590
• Main Authors: Xiang, Jianjun, Yu, Mei, Jiang, Gangyi, Xu, Haiyong, Song, Yang, Ho, Yo-Sung
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blind quality assessment; Eye (anatomy); Feature extraction; Field cameras; Image quality; light field image; Light fields; pseudo video; Quality assessment; refocused images; Shearlet transform; Support vector machines; Tensors; Two dimensional displays; Visual perception; Visualization
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2020.3030049

The commercial light field camera is able to capture four-dimensional Light Field Image (LFI), which can be visualized to LFI contents on 2D displays by means of the Pseudo Video (PV) or the Refocused Images (RIs) generated with the refocusing function of LFI. However, the quality degradation of LFI will affect user's visual experience of LFI contents. Hence, it is crucial to develop an effective LFI quality assessment method to monitor the LFI quality. Most existing subjective databases of LFI use PV and RIs visualization techniques to assess the quality of LFI. Therefore, as the way of presenting LFI on 2D display, PV and RIs are closely related to the subjective perception of LFI by human eyes. Based on these two visualization techniques, this article proposes a novel PV and RIs based blind LFI quality assessment method, in which the feature extraction is divided into two parts. In the first part, the PV's structure, motion and disparity information are extracted with multi-scale and multi-directional Shearlet transform. In the other part, the spatial structure, depth and semantic information of the RIs are obtained. Finally, support vector regression is used to nonlinear map the perceptual features to quality score of LFI. The experimental results on four LFI databases show that the proposed method has better correlation with human visual perception, compared with the classical 2D image quality assessment methods as well as the state-of-the-art LFI quality assessment methods.