基于自然統(tǒng)計特征分布的無參考圖像質量評價

陳勇; 帥鋒; 樊強

doi:10.11999/JEIT151058

基于自然統(tǒng)計特征分布的無參考圖像質量評價

doi: 10.11999/JEIT151058 cstr: 32379.14.JEIT151058

陳勇¹,
帥鋒¹,
樊強¹

基金項目:

國家自然科學基金(60975008)，重慶市教委科學技術研究項目(KJ1400434)

計量
- 文章訪問數: 1355
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- 被引次數: 0
出版歷程
- 收稿日期: 2015-09-14
- 修回日期: 2016-01-20
- 刊出日期: 2016-07-19

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

Funds:

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

摘要

摘要: 針對目前的無參考評價方法無法準確反映人類對圖像質量的視覺感知效果，該文提出一種基于自然統(tǒng)計特征分布(DIstribution Characteristics of Natural, DICN)的無參考圖像質量評價方法。其原理是用小波變換將圖像分解為低頻子帶和高頻子帶部分，再將高頻子帶部分分成的小塊，提取每一子塊的幅值和信息熵，并分別計算其分布直方圖均值和斜度作為特征，利用支持向量回歸思想對特征進行訓練，建立5種不同失真類型的質量預測模型。在此基礎上，采用支持向量機針對圖像特征構造分類器并進行失真判斷以確定不同失真的權重，結合5種失真評價模型可得到自然統(tǒng)計特征分布的無參考評價模型。實驗結果分析表明，該算法的評價效果優(yōu)于現有的經典算法，與主觀評價具有較好一致性，能夠準確反映人類對圖像質量的視覺感知效果。
- 無參考圖像質量評價 /
- 小波分解 /
- 局部幅值 /
- 局部熵
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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參考文獻(24)

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