Toward a No-Reference Quality Metric for Camera-Captured Images
| العنوان: | Toward a No-Reference Quality Metric for Camera-Captured Images |
|---|---|
| المؤلفون: | Xiongkuo Min, Ke Gu, Guangtao Zhai, Yutao Liu, Runze Hu |
| المصدر: | IEEE Transactions on Cybernetics. 53:3651-3664 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Source code, Computer science, business.industry, Image quality, media_common.quotation_subject, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Semantics, Computer Science Applications, Image (mathematics), Human-Computer Interaction, Support vector machine, Control and Systems Engineering, Metric (mathematics), Quality Score, Quality (business), Artificial intelligence, Electrical and Electronic Engineering, business, Software, Information Systems, media_common |
| الوصف: | 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. |
| تدمد: | 2168-2275 2168-2267 |
| DOI: | 10.1109/tcyb.2021.3128023 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67852b00f33a2e15f08212d0dd730db8 https://doi.org/10.1109/tcyb.2021.3128023 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi.dedup.....67852b00f33a2e15f08212d0dd730db8 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21682275 21682267 |
|---|---|
| DOI: | 10.1109/tcyb.2021.3128023 |