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基于內(nèi)在生成機制的多尺度結(jié)構(gòu)相似性圖像質(zhì)量評價

孫彥景,  楊玉芬,  劉東林,  施文娟

孫彥景, 楊玉芬, 劉東林, 施文娟. 基于內(nèi)在生成機制的多尺度結(jié)構(gòu)相似性圖像質(zhì)量評價[J]. 電子與信息學(xué)報, 2016, 38(1): 127-134. doi: 10.11999/JEIT150616
引用本文: 孫彥景, 楊玉芬, 劉東林, 施文娟. 基于內(nèi)在生成機制的多尺度結(jié)構(gòu)相似性圖像質(zhì)量評價[J]. 電子與信息學(xué)報, 2016, 38(1): 127-134. doi: 10.11999/JEIT150616
SUN Yanjing, YANG Yufen, LIU Donglin, SHI Wenjuan. Multiple-scale Structural Similarity Image Quality Assessment Based on Internal Generative Mechanism[J]. Journal of Electronics & Information Technology, 2016, 38(1): 127-134. doi: 10.11999/JEIT150616
Citation: SUN Yanjing, YANG Yufen, LIU Donglin, SHI Wenjuan. Multiple-scale Structural Similarity Image Quality Assessment Based on Internal Generative Mechanism[J]. Journal of Electronics & Information Technology, 2016, 38(1): 127-134. doi: 10.11999/JEIT150616

基于內(nèi)在生成機制的多尺度結(jié)構(gòu)相似性圖像質(zhì)量評價

doi: 10.11999/JEIT150616 cstr: 32379.14.JEIT150616
  • 2.

    (中國礦業(yè)大學(xué)信息與電氣工程學(xué)院 徐州 221116) ②(中國礦業(yè)大學(xué)江蘇省煤礦電氣與自動化工程實驗室 徐州 221008)

基金項目: 

江蘇省煤礦電氣與自動化工程實驗室建設(shè)項目(2014KJZX05，江蘇省產(chǎn)學(xué)研前瞻性聯(lián)合研究項目(BY2014028-01)，中央高校重大項目培育專項(2014ZDPY16)，國家自然科學(xué)基金(51274202)，江蘇省自然科學(xué)基金(2013-2016, BK20131124)和中央高校創(chuàng)新人才基金(2013RC11)

Multiple-scale Structural Similarity Image Quality Assessment Based on Internal Generative Mechanism

  • 2.

    (School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Funds: 

The Jiangsu Province Laboratory of Electrical and Automation Engineering for Coal Mining (2014KJZX05), The Perspective Research Foundation of Production Study and Research Alliance of Jiangsu Province (BY2014028-01), The Fundamental Research Foundation for the Central Universities (2014ZDPY16), The National Natural Science Foundation of China (51274202), The Natural Science Foundation of Jiangsu Province (BK201311240), The Fundamental Research Funds for the Central Universities (2013RC11)

  • 摘要: 該文針對多尺度結(jié)構(gòu)相似性(Multiple-scale Structural SIMilarity, MSSIM)圖像質(zhì)量評價算法對圖像信息不確定部分度量能力的不足，結(jié)合人類視覺系統(tǒng)(HVS)，提出基于內(nèi)在生成機制(internal generative mechanism)的iMSSIM算法。首先采用基于逐段式自回歸(Piecewise AutoRegressive, PAR)模型的內(nèi)在生成機制將失真圖像和原始圖像分解成采用MSSIM算法評分的圖像內(nèi)容預(yù)測部分和采用PSNR評分的圖像信息不確定部分；然后采用均方誤差(MSE)進(jìn)行加權(quán)來聯(lián)合這兩部分評分獲得最終結(jié)果。在基準(zhǔn)數(shù)據(jù)庫上完成的對比實驗表明：該算法不僅在不同失真類型上性能最好，且在6個公開數(shù)據(jù)庫上的性能優(yōu)于現(xiàn)有算法。
    Abstract: In order to improve image information uncertainty measurement of the Multiple-scale Structural SIMilarity (MSSIM), a novel algorithm called iMSSIM based on internal generative mechanism is proposed, combining with Human Visual System (HVS). Firstly, internal generative mechanism based on the Piecewise AutoRegressive (PAR) model decomposes distorted image and the original image into two parts, the predicted part of image content using MSSIM algorithm assessment and image information uncertainty Part using PSNR assessment. Then, Mean Square Error is used as weight to combine the two scores to acquire the overall image quality assessmet results. Experiments performed on benchmark IQA databases demonstrate that the proposed algorithm not only has the best performance in different types of distortion, but also is better than the existing algorithms.
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出版歷程
  • 收稿日期:  2015-05-25
  • 修回日期:  2015-08-21
  • 刊出日期:  2016-01-19

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