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基于高維度特征分析的非局部圖像質(zhì)量評(píng)價(jià)方法

丁勇,  李楠

丁勇, 李楠. 基于高維度特征分析的非局部圖像質(zhì)量評(píng)價(jià)方法[J]. 電子與信息學(xué)報(bào), 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430
引用本文: 丁勇, 李楠. 基于高維度特征分析的非局部圖像質(zhì)量評(píng)價(jià)方法[J]. 電子與信息學(xué)報(bào), 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430
DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430
Citation: DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430

基于高維度特征分析的非局部圖像質(zhì)量評(píng)價(jià)方法

doi: 10.11999/JEIT151430 cstr: 32379.14.JEIT151430
基金項(xiàng)目: 

國(guó)家863計(jì)劃(2015AA016704c)，浙江省自然科學(xué)基金(LY14F020028)

Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis

Funds: 

Items: The National 863 Program of China (2015AA016704c), Zhejiang Provincial Natural Science Foundation (LY14F020028)

  • 摘要: 傳統(tǒng)的圖像質(zhì)量評(píng)價(jià)方法通常提取低維度特征即圖像的片面信息用來(lái)分析圖像質(zhì)量。高維度特征盡管不易分析但保留了更多信息，更利于全面分析圖像質(zhì)量。針對(duì)這種現(xiàn)狀，該文提出一種優(yōu)化數(shù)據(jù)采樣后基于高維度特征分析的圖像質(zhì)量評(píng)價(jià)方法。首先對(duì)圖像數(shù)據(jù)采樣分別利用塊匹配進(jìn)行篩選，用主成分分析進(jìn)行降維，其次利用核獨(dú)立分量分析從圖像數(shù)據(jù)采樣中提取高維度特征，最后基于自然圖像統(tǒng)計(jì)特性對(duì)特征進(jìn)行綜合得出圖像質(zhì)量。實(shí)驗(yàn)結(jié)果表明所提方法與人的主觀評(píng)價(jià)較為一致。
    Abstract: Traditionally, low dimensional features for partial information are extracted to analyze image quality. Though high dimensional features are difficult to be analyzed, they contain more information to fully analyze image quality. On this condition, this paper proposes an image quality assessment method based on non-local high dimensional feature analysis after optimized data sampling. Firstly, image data is filtered by using block matching method and dimensionally reduced by Principal Component Analysis (PCA). Secondly, Kernel Independent Component Analysis (KICA) is applied to extract high dimensional features. The features are finally synthesized to evaluate image quality based on natural image statistics. The experimental results show that the proposed method keeps accordance with human objective perception.
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出版歷程
  • 收稿日期:  2015-12-17
  • 修回日期:  2016-04-19
  • 刊出日期:  2016-09-19

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