Toward a No-Reference Quality Metric for Camera-Captured Images

Runze Hu, Yutao Liu*, Ke Gu, Xiongkuo Min, Guangtao Zhai

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

20 引用 (Scopus)
Plum Print visual indicator of research metrics
  • Citations
    • Citation Indexes: 20
  • Captures
    • Readers: 7
see details

摘要

Existing no-reference (NR) image quality assessment (IQA) metrics are still not convincing for evaluating the quality of the camera-captured images. Toward tackling this issue, we, in this article, establish a novel NR quality metric for quantifying the quality of the camera-captured images reliably. Since the image quality is hierarchically perceived from the low-level preliminary visual perception to the high-level semantic comprehension in the human brain, in our proposed metric, we characterize the image quality by exploiting both the low-level image properties and the high-level semantics of the image. Specifically, we extract a series of low-level features to characterize the fundamental image properties, including the brightness, saturation, contrast, noiseness, sharpness, and naturalness, which are highly indicative of the camera-captured image quality. Correspondingly, the high-level features are designed to characterize the semantics of the image. The low-level and high-level perceptual features play complementary roles in measuring the image quality. To infer the image quality, we employ the support vector regression (SVR) to map all the informative features to a single quality score. Thorough tests conducted on two standard camera-captured image databases demonstrate the effectiveness of the proposed quality metric in assessing the image quality and its superiority over the state-of-the-art NR quality metrics. The source code of the proposed metric for camera-captured images is released at https://github.com/YT2015?tab=repositories.

源语言英语
页(从-至)3651-3664
页数14
期刊IEEE Transactions on Cybernetics
53
6
DOI
出版状态已出版 - 1 6月 2023
已对外发布

指纹

探究 'Toward a No-Reference Quality Metric for Camera-Captured Images' 的科研主题。它们共同构成独一无二的指纹。

引用此

Hu, R., Liu, Y., Gu, K., Min, X., & Zhai, G. (2023). Toward a No-Reference Quality Metric for Camera-Captured Images. IEEE Transactions on Cybernetics, 53(6), 3651-3664. https://doi.org/10.1109/TCYB.2021.3128023