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基于非負(fù)矩陣分解的彩色圖像質(zhì)量評價(jià)方法

徐海勇,  郁梅,  駱挺,  呂亞奇,  蔣剛毅

徐海勇, 郁梅, 駱挺, 呂亞奇, 蔣剛毅. 基于非負(fù)矩陣分解的彩色圖像質(zhì)量評價(jià)方法[J]. 電子與信息學(xué)報(bào), 2016, 38(3): 578-585. doi: 10.11999/JEIT150610
引用本文: 徐海勇, 郁梅, 駱挺, 呂亞奇, 蔣剛毅. 基于非負(fù)矩陣分解的彩色圖像質(zhì)量評價(jià)方法[J]. 電子與信息學(xué)報(bào), 2016, 38(3): 578-585. doi: 10.11999/JEIT150610
XU Haiyong, YU Mei, LUO Ting, Lü Yaqi, JIANG Gangyi. A Color Image Quality Assessment Method Based onNon-negative Matrix Factorization[J]. Journal of Electronics & Information Technology, 2016, 38(3): 578-585. doi: 10.11999/JEIT150610
Citation: XU Haiyong, YU Mei, LUO Ting, Lü Yaqi, JIANG Gangyi. A Color Image Quality Assessment Method Based onNon-negative Matrix Factorization[J]. Journal of Electronics & Information Technology, 2016, 38(3): 578-585. doi: 10.11999/JEIT150610

基于非負(fù)矩陣分解的彩色圖像質(zhì)量評價(jià)方法

doi: 10.11999/JEIT150610 cstr: 32379.14.JEIT150610
  • 2.

    (寧波大學(xué)信息科學(xué)與工程學(xué)院 寧波 315211) ②(寧波大學(xué)科學(xué)技術(shù)學(xué)院 寧波 315212)

基金項(xiàng)目: 

國家自然科學(xué)基金 (U1301257, 61171163, 61271270, 61271021, 61311140262, 61501270)，浙江省自然科學(xué)基金(LY14F010004, LY15F010005)，浙江省重中之重學(xué)科開放基金

A Color Image Quality Assessment Method Based onNon-negative Matrix Factorization

  • 2.

    (Faculty of Information Science and Engineering, Ningbo University, Ningbo 315211, China)

Funds: 

The National Natural Science Foundation of China (U1301257, 61171163, 61271270, 61271021, 61311140262, 61501270), Zhejiang Provincial Natural Science Foundation of China (LY14F010004, LY15F010005), Open Fund of Zhejiang Provincial Key Academic Project (first level)

  • 摘要: 針對稀疏表示的圖像質(zhì)量評價(jià)模型都基于灰度圖像，缺少顏色信息，該文提出一種基于非負(fù)矩陣分解(NMF)的全參考彩色圖像質(zhì)量評價(jià)方法。首先，從自然彩色圖像中隨機(jī)采樣，得到訓(xùn)練樣本，利用非負(fù)矩陣分解，訓(xùn)練得到特征基矩陣，并經(jīng)過Schmidt正交化，構(gòu)建特征提取矩陣；其次，根據(jù)視覺顯著性模型，利用最大視覺顯著性和顯著性差值兩步驟選取視覺重要區(qū)域；最后，利用特征提取矩陣，得到低維的特征向量，并最終得到彩色圖像質(zhì)量評價(jià)值。實(shí)驗(yàn)結(jié)果表明，該文方法在LIVE, CSIQ和TID2008 3個(gè)圖像質(zhì)量評價(jià)庫上有很好的表現(xiàn)。3個(gè)圖像庫的平均結(jié)果顯示，該文方法的綜合表現(xiàn)優(yōu)于所有對比方法。這表明該文方法與主觀感知有更好的關(guān)聯(lián)度。
    Abstract: For the sparse representation of image quality assessment model are based on gray image and the lack of color information, a Non-negative Matrix Factorization (NMF)-based full reference color image quality assessment method is proposed. Firstly, from the natural color image in random sampling, training samples are got. Non-negative matrix factorization method is used to train and get a feature basis matrix. After using Schmidt orthogonalization, a feature extracting matrix is got. Secondly, according to the visual saliency model, maximum visual saliency is defined and significant difference of two steps is used to select visual important area. Finally, using the feature extraction matrix, low dimensional feature vectors and the final color image quality evaluation value are got. The experimental results show that the proposed method has good performance in the LIVE, CSIQ and TID2008 three image databases. The average results of three image quality assessment databases show that the proposed method outperforms other methods, which means that the proposed method has better correlation with the subjective perception.
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出版歷程
  • 收稿日期:  2015-05-25
  • 修回日期:  2015-11-09
  • 刊出日期:  2016-03-19

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