Evaluation of no-reference models to assess image sharpness

In the past decades, massive attention has been paid toward no-reference or blind image sharpness assessment (BISA) and many algorithms have achieved good performance. This paper provides an evaluation of 12 state-of-the-art BISA methods based on Gaussian blurring images collected from four simulati...

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Bibliographic Details
Published in2017 IEEE International Conference on Information and Automation (ICIA) pp. 683 - 687
Main Authors Guangzhe Dai, Zhaoyang Wang, Yaoqing Li, Qian Chen, Shaode Yu, Yaoqin Xie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Fish
Gaussian blurring
Image edge detection
image quality assessment
Image sharpness assessment
Measurement
no-reference
Prediction algorithms
Real-time systems
Static VAr compensators
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Summary:In the past decades, massive attention has been paid toward no-reference or blind image sharpness assessment (BISA) and many algorithms have achieved good performance. This paper provides an evaluation of 12 state-of-the-art BISA methods based on Gaussian blurring images collected from four simulation databases (LIVE, CSIQ, TID2008 and TID2013). The prediction performance is estimated with two metrics after four-or five-parameter non-linear score fitting. Experimental results indicate that the algorithm RISE achieves the best performance. Additionally, the effect of different non-linear scoring fitting methods on the performance evaluation is insignificant. In general, RISE is a visible and significant milestone for BISA algorithm development at present and the future work might be toward novel and real-life applications.
DOI:10.1109/ICInfA.2017.8078993