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                    結合全局與局部變化的圖像質量評價

                    高敏娟 黨宏社 魏立力 王海龍 張選德

                    高敏娟, 黨宏社, 魏立力, 王海龍, 張選德. 結合全局與局部變化的圖像質量評價. 自動化學報, 2020, 46(12): 2662?2671 doi: 10.16383/j.aas.c190697
                    引用本文: 高敏娟, 黨宏社, 魏立力, 王海龍, 張選德. 結合全局與局部變化的圖像質量評價. 自動化學報, 2020, 46(12): 2662?2671 doi: 10.16383/j.aas.c190697
                    Gao Min-Juan, Dang Hong-She, Wei Li-Li, Wang Hai-Long, Zhang Xuan-De. Combining global and local variation for image quality assessment. Acta Automatica Sinica, 2020, 46(12): 2662?2671 doi: 10.16383/j.aas.c190697
                    Citation: Gao Min-Juan, Dang Hong-She, Wei Li-Li, Wang Hai-Long, Zhang Xuan-De. Combining global and local variation for image quality assessment. Acta Automatica Sinica, 2020, 46(12): 2662?2671 doi: 10.16383/j.aas.c190697

                    結合全局與局部變化的圖像質量評價

                    doi: 10.16383/j.aas.c190697
                    基金項目: 國家自然科學基金(61871260, 61871259), 陜西科技大學人工智能交叉學科PI團隊培育專項基金資助
                    詳細信息
                      作者簡介:

                      高敏娟:陜西科技大學電氣與控制工程學院博士研究生. 2010年獲得山西大學工學碩士學位. 主要研究方向為圖像處理, 圖像質量評價.E-mail: gaominjuan1984@163.com

                      黨宏社:陜西科技大學電氣與控制工程學院教授. 主要研究方向為工業過程與優化, 計算機控制, 圖像處理.E-mail: danghs@sust.edu.cn

                      魏立力:寧夏大學數學統計學院教授. 主要研究方向為應用統計與數據分析. E-mail: liliwei@nxu.edu.cn

                      王海龍:寧夏師范學院數學與計算機科學學院講師. 2011年獲得香港公開大學教育碩士學位. 主要研究方向為代數.E-mail: wanghailong7903@163.com

                      張選德:陜西科技大學電子信息與人工智能學院教授. 2013年獲得西安電子科技大學理學博士學位. 主要研究方向為圖像恢復, 圖像質量評價, 稀疏表示和低秩逼近理論. 本文通信作者.E-mail: zhangxuande@sust.edu.cn

                    Combining Global and Local Variation for Image Quality Assessment

                    Funds: Supported by National Natural Science Foundation of China (61871260, 61871259) and Shaanxi University of Science and Technology Artificial Intelligence Interdisciplinary PI Team Cultivation Special Project
                    • 摘要: 圖像所包含的信息是通過灰度值在空域的變化呈現的. 梯度是度量變化的基本工具, 這使得梯度成為了目前大多數圖像質量評價算法的重要組成部分. 但是梯度只能度量局部變化, 而當人類視覺系統(Human visual system, HVS)感知一幅圖像時, 既能感知到局部變化, 也能感知到全局變化. 基于HVS的這一特性, 本文提出了一種結合全局與局部變化的圖像質量評價算法(Global and local variation similarity, GLV-SIM). 該算法利用Grünwald-Letnikov分數階導數來度量圖像的全局變化, 利用梯度模來度量圖像的局部變化. 然后結合二者計算參考圖像和退化圖像之間的相似度譜(Similarity map), 進而得到圖像的客觀評分. 在TID2013、TID2008、CSIQ與LIVE四個數據庫上的仿真實驗表明, 較之單一度量局部變化的方法, 本文算法能更準確地模擬HVS對圖像質量的感知過程, 給出的客觀評分與主觀評分具有較好的一致性.
                    • 圖  1  Child-swimming圖像

                      Fig.  1  The image of child-swimming

                      圖  2  GLV-SIM算法框架

                      Fig.  2  The framework of GLV-SIM algorithm

                      圖  3  參考圖像(a)及其不同類型退化圖像(b)~(f) (右下角為矩形區域局部放大圖)

                      Fig.  3  Reference image (a) and different types of distorted images (b)~(f)

                      (The lower right corner is a enlarged view of the rectangular region)

                      圖  4  針對圖3中各矩形區域對應的$DM$

                      Fig.  4  The corresponding $DM$ map for each rectangular region in Fig.3

                      表  1  圖3(b)~(f)主觀評分和不同算法客觀評分

                      Table  1  Subjective scores and objective scores of different algorithms for Fig. 3(b)~(f)

                      評價方法圖3(b)圖3(c)圖3(d)圖3(e)圖3(f)
                      MOS5.00003.83874.18754.76676.2903
                      PSNR30.530430.578426.130327.480827.3498
                      VSNR29.730121.148020.534230.707220.2681
                      IFC4.73893.43514.93192.995611.3746
                      SSIM 0.92500.84610.94590.94750.9568
                      MS-SSIM 0.96060.91590.97380.97270.9821
                      IW-SSIM0.96840.90750.96450.97040.9661
                      GSIM0.99580.98880.99530.99660.9979
                      FSIM0.98310.94620.95380.96990.9707
                      GLV-SIM0.99590.98450.99270.99570.9961
                      下載: 導出CSV

                      表  2  針對表1評分排名

                      Table  2  The rank of scores on Table 1

                      評價方法圖3(b)圖3(c)圖3(d)圖3(e)圖3(f)
                      MOS25431
                      PSNR21534
                      VSNR23415
                      IFC34251
                      SSIM45321
                      MS-SSIM45231
                      IW-SSIM24513
                      GSIM35421
                      FSIM15432
                      GLV-SIM25431
                      下載: 導出CSV

                      表  3  不同IQA算法在TID2013和TID2008數據庫的實驗結果比較

                      Table  3  Comparison the performance results of different IQA algorithms on TID2013 and TID2008 databases

                      數據庫性能指標PSNRVSNRIFCSSIMMS-SSIMIW-SSIMGSIMFSIMGLV-SIM
                      TID 2013SROCC0.63960.68120.53890.74170.78590.77790.79460.80150.8068
                      KROCC0.46980.50840.39390.55880.60470.59770.62550.62890.6381
                      PLCC0.70170.74020.55380.78950.83290.83190.84640.85890.8580
                      RMSE0.88320.83921.03220.76080.68610.68800.66030.63490.6368
                      TID 2008SROCC0.55310.70460.56750.77490.85420.85590.85040.88050.8814
                      KROCC0.40270.53400.42360.57680.65680.66360.65960.69460.6956
                      PLCC0.57340.68200.73400.77320.84510.85790.84220.87380.8648
                      RMSE1.09940.98150.91130.85110.71730.68950.72350.65250.6739
                      下載: 導出CSV

                      表  4  不同IQA算法在CSIQ和LIVE數據庫的實驗結果比較

                      Table  4  Comparison the performance results of different IQA algorithms on CSIQ and LIVE databases

                      數據庫性能指標PSNRVSNRIFCSSIMMS-SSIMIW-SSIMGSIMFSIMGLV-SIM
                      CSIQSROCC0.80580.81060.76710.87560.91330.92130.91080.92420.9264
                      KROCC0.60840.62470.58970.69070.73930.75290.73740.75670.7605
                      PLCC0.80000.80020.83840.86130.89910.91440.89640.91200.9082
                      RMSE0.15750.15750.14310.13340.11490.10630.11640.10070.1099
                      LIVESROCC0.87560.92740.92590.94790.95130.95670.95610.96340.9521
                      KROCC0.68650.76160.75790.79630.80450.81750.81500.83370.8179
                      PLCC0.87230.92310.92680.94490.94890.95220.95120.95970.9368
                      RMSE13.35910.50510.2648.94458.61888.34738.43277.67808.0864
                      下載: 導出CSV

                      表  5  不同IQA算法在TID2008數據庫單一失真評價性能(SROCC)比較

                      Table  5  Comparison SROCC for individual distortion of different IQA algorithms on TID2008 database

                      數據庫失真類型PSNRVSNRIFCSSIMMS-SSIMIW-SSIMGSIMFSIMGLV-SIM
                      TID 2008AWN0.90730.77280.58060.81070.80940.78690.85730.85660.9125
                      ANMC0.89940.77930.54600.80290.80640.79200.80950.85270.8979
                      SCN0.91750.76650.59580.81440.81950.77140.89020.84830.9167
                      MN0.85200.72950.67320.77950.81560.80870.74030.80210.8087
                      HFN0.92730.88110.73180.87290.86850.86620.89320.90930.9175
                      IMN0.87250.64700.53450.67320.68680.64650.77210.74520.7864
                      QN0.87020.82710.58570.85310.85370.81770.87500.85640.8865
                      GB0.87040.93300.85590.95440.96070.96360.95850.94720.9587
                      DEN0.94220.92860.79730.95300.95710.94730.97230.96030.9666
                      JPEG0.87230.91740.81800.92520.93480.91840.93910.92790.9534
                      JP2K0.81310.95150.94370.96250.97360.97380.97550.97730.9751
                      JGTE0.75250.80560.79090.86780.87360.85880.88320.87080.8793
                      J2TE0.83120.79090.73010.85770.85220.82030.89250.85440.9021
                      NEPN0.58120.57160.84180.71070.73360.77240.73720.74910.7271
                      BLOCK0.61940.19260.67700.84620.76170.76230.88650.84920.8960
                      MS0.69660.37150.42500.72310.73740.70670.71740.66980.6994
                      CTC0.58670.42390.27130.52460.63980.63010.67360.64810.6689
                      下載: 導出CSV
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                    • 收稿日期:  2019-10-08
                    • 錄用日期:  2019-12-15
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