期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷 31, 期 4, 页码 1332-1341出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2020.3002662
关键词
Distortion; Image quality; Image restoration; Degradation; Feature extraction; Task analysis; Visualization; No-reference image quality assessment; domain fingerprints; generative adversarial network
资金
- Major Research Plan of the National Natural Science Foundation of China [61991451]
- Shenzhen Special Fund for the Strategic Development of Emerging Industries [JCYJ20170412170118573]
This study introduces a new no-reference image quality assessment method, incorporating the concept of domain fingerprint. By designing a new domain-aware architecture, the method is able to simultaneously determine the distortion sources and quality of an image. Experimental results show that the proposed method outperforms most existing state-of-the-art NR-IQA methods.
Human fingerprints are detailed and nearly unique markers of human identity. Such a unique and stable fingerprint is also left on each acquired image. It can reveal how an image was degraded during the image acquisition procedure and thus is closely related to the quality of an image. In this work, we propose a new no-reference image quality assessment (NR-IQA) approach called domain-aware IQA (DA-IQA), which for the first time introduces the concept of domain fingerprint to the NR-IQA field. The domain fingerprint of an image is learned from image collections of different degradations and then used as the unique characteristics to identify the degradation sources and assess the quality of the image. To this end, we design a new domain-aware architecture, which enables simultaneous determination of both the distortion sources and the quality of an image. With the distortion in an image better characterized, the image quality can be more accurately assessed, as verified by extensive experiments, which show that the proposed DA-IQA performs better than almost all the compared state-of-the-art NR-IQA methods.
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