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基于小波分析的圖像稀疏保真度評價

陳勇 樊強 帥鋒

陳勇, 樊強, 帥鋒. 基于小波分析的圖像稀疏保真度評價[J]. 電子與信息學(xué)報, 2015, 37(9): 2055-2061. doi: 10.11999/JEIT150173
引用本文: 陳勇, 樊強, 帥鋒. 基于小波分析的圖像稀疏保真度評價[J]. 電子與信息學(xué)報, 2015, 37(9): 2055-2061. doi: 10.11999/JEIT150173
Chen Yong, Fan Qiang, Shuai Feng. Sparse Image Fidelity Evaluation Based on Wavelet Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2055-2061. doi: 10.11999/JEIT150173
Citation: Chen Yong, Fan Qiang, Shuai Feng. Sparse Image Fidelity Evaluation Based on Wavelet Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2055-2061. doi: 10.11999/JEIT150173

基于小波分析的圖像稀疏保真度評價

doi: 10.11999/JEIT150173
基金項目: 

國家自然科學(xué)基金(60975008)和重慶市教委科學(xué)技術(shù)研究項目(KJ1400434)

Sparse Image Fidelity Evaluation Based on Wavelet Analysis

  • 摘要: 該文針對傳統(tǒng)的圖像質(zhì)量評價方法無法有效模擬人類視覺系統(tǒng)(HVS)存在的不足,提出基于小波分析的加權(quán)稀疏保真度(Weighting Sparse Fidelity, WSF)圖像評價算法。算法以模擬人類視覺系統(tǒng)的神經(jīng)網(wǎng)絡(luò)為切入點,對圖像進行一階小波分解得到4個不同方向的子帶圖像,然后將子帶圖像分成88大小的圖像塊,采用快速獨立分量分析(FastICA)的方法對各個圖像塊進行訓(xùn)練并提取圖像特征檢測矩陣,根據(jù)特征檢測矩陣計算各子帶圖像塊的稀疏特征值并建立稀疏保真度質(zhì)量評價模型。在此基礎(chǔ)上,根據(jù)細節(jié)信息的不同對低頻子帶圖像進行區(qū)間劃分并設(shè)置視覺權(quán)重,使之更加接近人眼的主觀視覺。實驗中對LIVE庫中所有圖像進行算法驗證,其結(jié)果表明,所提方法能很好地對各種失真類型的圖像進行評價。基于小波分析的稀疏保真度評價算法能夠有效模擬人類視覺系統(tǒng)的多頻特性和視覺皮層感知機制,彌補現(xiàn)有圖像質(zhì)量評價方法在此方面的不足。
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  • 文章訪問數(shù):  1531
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  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2015-01-30
  • 修回日期:  2015-05-05
  • 刊出日期:  2015-09-19

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