Research on the Key Technology of Quality Assessment for Light Field Images

报告人简介:

   马健，男，博士（后），讲师，硕士生导师。2018年6月毕业于上海大学通信与信息工程学院信号与信息处理专业，获工学博士学位，2018年8月至2019年12月在上海海事大学物流科学与工程研究院从事教学和科研工作。2020年1月至今，在安徽大学互联网学院从事教学和科研工作。2020年10月至今，在复旦大学计算机科学技术学院计算机应用技术博士后流动站从事博士后研究工作。

   主要研究方向为沉浸式的多媒体计算、人工智能理论及其应用；主持国家自然基金青年基金项目一项，中国博士后面上基金一项，上海市教委“上海高校青年教师培养资助计划”项目一项；安徽大学博士科研启动项目一项；参与国家自然科学基金面上项目三项，国家自然基金青年基金一项，安徽省面上基金项目一项，企业横向课题多项。近年来，已发表SCI/ EI论文20余篇。

摘要:Light field image (LFI) now is becoming increasingly popular in immersive media applications. Unlike traditional 2D and 3D images, images taken by light field cameras can capture both angular and spatial information. However, the spatial and angular information of LFI is highly inter-twined with varying disparities, which poses a higher challenge to the quality assessment of LFI. To address this issue, this paper proposes a full-reference light field image quality assessment (LFIQA) index that attempts to disentangle the coupling information from macro-pixel image (MacPI) to accurately evaluate the entire LFI quality. The proposed framework can be divided into three steps. Firstly, the LFIs are converted into the MacPIs, and then the spatial and angular feature maps are disentangled by using the spatial, angular and epipolar plane image (EPI) convolutions in the MacPI mode. Secondly, the structural similarity (SSIM) maps are calculated between the disentangled feature maps of the original and distorted LFIs. Furthermore, the quality-aware features of LFIs are extracted on the SSIM maps by utilized local binary patterns (LBP) and natural scene statistics (NSS). Finally, support vector regression (SVR) is utilized to predict the qualities of LFIs. Extensive experiments show that the proposed model outperforms multiple classical and state-of-the-art methods.