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Proceedings Paper

Color image definition evaluation method based on deep learning method
Author(s): Di Liu; YingChun Li
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Paper Abstract

In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

Paper Details

Date Published: 10 January 2018
PDF: 6 pages
Proc. SPIE 10616, 2017 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments, 106160V (10 January 2018); doi: 10.1117/12.2289589
Show Author Affiliations
Di Liu, Academy of Equipment (China)
YingChun Li, Academy of Equipment (China)


Published in SPIE Proceedings Vol. 10616:
2017 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments
Yongtian Wang; Baohua Jia; Kimio Tatsuno; Liquan Dong, Editor(s)

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