A No-reference Image Quality Assessment Based on Distribution Characteristics of Natural Statistics

CHEN Yong; SHUAI Feng; FAN Qiang

doi:10.11999/JEIT151058

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1645-1653

CHEN Yong, SHUAI Feng, FAN Qiang. A No-reference Image Quality Assessment Based on Distribution Characteristics of Natural Statistics[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1645-1653. doi: 10.11999/JEIT151058

Citation:

CHEN Yong, SHUAI Feng, FAN Qiang. A No-reference Image Quality Assessment Based on Distribution Characteristics of Natural Statistics[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1645-1653. doi: 10.11999/JEIT151058

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A No-reference Image Quality Assessment Based on Distribution Characteristics of Natural Statistics

doi: 10.11999/JEIT151058

Funds:

The National Natural Science Foundation of China (60975008), Science and Technology Research Project of Chongqing Education Committee (KJ1400434)

Received Date: 2015-09-14
Rev Recd Date: 2016-01-20
Publish Date: 2016-07-19

Abstract

Abstract

The current No-Reference Image Quality Assessment (NR-IQA) methods are not well consistent with subjective evaluation, a novel NR-IQA method based on the DIstribution Characteristics of Natural statistics (DICN) is proposed in this paper. In the proposed method, image is decomposed into low frequency subbands and high frequency subbands with wavelet, and its high frequency subbands are divided into blocks at size of 88, their amplitude and entropy are respectively extracted from the blocks, then their mean values of the distribution histogram and skewness are respectively calculated, and their results are as the image features. The features trained by Support Vector Regression (SVR) are for building 5 kinds of distortion image quality pre-measurement model. To determine the weights of the different distortions, the image features of classifier based on SVR are structured for carrying out the distortion evalution. Based on 5 kinds of distortion evaluation models, the NR-IQA model with the natural statistical distribution can be obtained. The results of experiments show that the proposed method performance is better than the present classical methods. The method is well consistent with the subjective assessment results, and can reflect human subjective feeling well.
- No-Reference Image Quality Assessment (NR-IQA),
- Wavelet decomposition,
- Local amplitude,
- Local entropy

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References(24)

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