Imaging Components, Systems, and Processing

Contrast sensitivity function calibration based on image quality prediction

[+] Author Affiliations
Yu Han

Jiangsu Automation Research Institute, Shenghu Road 18, Lianyungang 222006, China

Yunze Cai

Shanghai Jiao Tong University, Automation Department, Dongchuan Road 800, Shanghai 200240, China

Opt. Eng. 53(11), 113107 (Nov 11, 2014). doi:10.1117/1.OE.53.11.113107
History: Received July 25, 2014; Revised October 2, 2014; Accepted October 7, 2014
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Abstract.  Contrast sensitivity functions (CSFs) describe visual stimuli based on their spatial frequency. However, CSF calibration is limited by the size of the sample collection and this remains an open issue. In this study, we propose an approach for calibrating CSFs that is based on the hypothesis that a precise CSF model can accurately predict image quality. Thus, CSF calibration is regarded as the inverse problem of image quality prediction according to our hypothesis. A CSF could be calibrated by optimizing the performance of a CSF-based image quality metric using a database containing images with known quality. Compared with the traditional method, this would reduce the work involved in sample collection dramatically. In the present study, we employed three image databases to optimize some existing CSF models. The experimental results showed that the performance of a three-parameter CSF model was better than that of other models. The results of this study may be helpful in CSF and image quality research.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Yu Han and Yunze Cai
"Contrast sensitivity function calibration based on image quality prediction", Opt. Eng. 53(11), 113107 (Nov 11, 2014). ; http://dx.doi.org/10.1117/1.OE.53.11.113107


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