期刊
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
卷 14, 期 1, 页码 64-77出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTSP.2019.2955024
关键词
Image quality; Image coding; Distortion; Streaming media; Quality assessment; Indexes; 360-degree images; image quality assessment; convolution neural network; hyper-structure
资金
- National Natural Science Foundation of China [61831015, 61527804, 61521062, 61901260, 61771305, 61927809]
- China Postdoctoral Science Foundation [BX20180197, 2019M651496]
360-degree images/videos have been dramatically increasing in recent years. The characteristic of omnidirectional-view results in high resolution of 360-degree images/videos, which makes them difficult to be transported and stored. To deal with the problem, video coding technologies are used to compress the omnidirectional content but they will introduce the compression distortion. Therefore, it is important to study how popular coding technologies affect the quality of 360-degree images. In this paper, we present a study on both subjective and objective quality assessment of compressed virtual reality (VR) images. We first build a compressed VR image quality (CVIQ) database including 16 reference images and 528 compressed ones with three prevailing coding technologies. Then, we propose a multi-channel convolution neural network (CNN) for blind 360-degree image quality assessment (MC360IQA). To be consistent with the visual content seen in the VR device, we project each 360-degree image into six viewport images, which are adopted as inputs of the proposed model. MC360IQA consists of two parts, a multi-channel CNN and an image quality regressor. The multi-channel CNN includes six parallel hyper-ResNet34 networks, where the hyper structure is used to incorporate the features from intermediate layers. The image quality regressor fuses the features and regresses them to final scores. The experimental results show that our model achieves the best performance among the state-of-art full-reference (FR) and no-reference (NR) image quality assessment (IQA) models on the CVIQ database and other available 360-degree IQA database.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据