No-reference image sharpness assessment via difference quotients
| Autor: | Jin Fu, Wei Song, Jide Qian, Jiye Qian, Hengjun Zhao, Xiao Qianbo |
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| Rok vydání: | 2019 |
| Předmět: |
Pixel
Image quality business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Digital imaging Image processing Pattern recognition 02 engineering and technology Absolute difference Atomic and Molecular Physics and Optics Computer Science Applications Computer Science::Computer Vision and Pattern Recognition Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business Difference quotient Image compression |
| Zdroj: | Journal of Electronic Imaging. 28:1 |
| ISSN: | 1017-9909 |
| DOI: | 10.1117/1.jei.28.1.013032 |
| Popis: | Sharpness is an important indicator to evaluate image quality or to optimize parameters in computer vision tasks, such as image acquisition, compression, and restoration. We utilize difference quotients to construct an absolute difference quotient and a relative difference quotient to evaluate the sharpness among images containing difference contents and the sharpness among pixels in the same image, respectively. Based on the constructed quotients, we estimate the pixel sharpness index and the image block sharpness index and create a single sharpness index as the overall sharpness of an image by pooling strategy. Our quotient-based methods can assess image sharpness effectively and efficiently. Experimental results on four simulated databases with real blurring and synthetic blurring images show the proposed sharpness metric is consistent with subjective sharpness evaluations and is competitive with existing sharpness metrics. It achieves a balance between running time and high performance. |
| Databáze: | OpenAIRE |
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