Lin Zhang等人在論文《A COMPREHENSIVEEVALUATION OF FULL REFERENCE IMAGE QUALITY ASSESSMENT ALGORITHMS》中，比較了幾種全參考圖像質量評價算法，在此記錄一下他們的結果。
下表所示是他們所用的圖像庫，包含了：TID2008database，CSIQ database，LIVEdatabase，IVCdatabase，Toyama-MICTdatabase，Cornell A57 database，以及 WirelessImaging Quality database (WIQ)。從上到下數據庫的規模依次下降。
一共比較了如下所列的全參考圖像客觀質量評價算法：
PeakSignal to Noise Ratio(PSNR)
峰值信噪比。
noise quality measure (NQM) index
參考文獻：N. Damera-Venkata, T.D. Kite, W.S. Geisler, B.L. Evans, and A.C.Bovik, “Image quality assessment based on a degradation model,” IEEE Trans. IP,vol. 9, pp. 636-650, 2000.
universal quality index (UQI)
參考文獻：Z. Wang and A.C. Bovik, “A universal image quality index,” IEEE SignalProcess. Lett., vol. 9, pp. 81-84, 2002.
structural similarity (SSIM) index
參考文獻：Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli,”Image qualityassessment: from error visibility to structural similarity,” IEEE Trans. IP,vol. 13, pp. 600-612, 2004.
multi-scaleSSIM (MS-SSIM) index
參考文獻：Z. Wang, E.P. Simoncelli, and A.C. Bovik, “Multi-scale structuralsimilarity for image quality assessment,” ACSSC’03, pp. 1398-1402, 2003.
information fidelity criterion (IFC) index
參考文獻：H.R. Sheikh, A.C. Bovik, and G. de Veciana, “An information fidelitycriterion for image quality assessment using natural scene statistics,” IEEETrans. IP, vol. 14, pp. 2117-2128, 2005.
visual information fidelity (VIF) index
參考文獻：H.R. Sheikh and A.C. Bovik, “Image information and visual quality,”IEEE Trans. IP, vol. 15, pp. 430-444, 2006.
visual signal to noise ratio (VSNR) index
參考文獻：D.M. Chandler and S.S. Hemami, “VSNR: a wavelet-based visualsignal-to-noise ratio for natural images,” IEEE Trans. IP, vol. 16, pp.2284-2298, 2007.
information content weighted SSIM (IW-SSIM) index
參考文獻：Z. Wang and Q. Li, “Information content weighting for perceptualimage quality assessment,” IEEE Trans. IP, vol. 20,
pp. 1185-1198, 2011.
Riesz transforms based feature similarity (RFSIM) index
參考文獻：L. Zhang, L. Zhang, and X. Mou, “RFSIM: a feature based imagequality assessment metric using Riesz transforms,” ICIP’10, pp. 321-324, 2010.
feature similarity (FSIM) index
參考文獻：L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarityindex for image quality assessment,” IEEE Trans. IP, vol. 20, pp. 2378-2386,2011.
統計了每種全參考圖像質量評價算法的客觀值和主觀值之間的相關系數：
斯皮爾曼秩相關系數（Spearman rankorder correlation coefficient，SROCC），肯德爾秩次相關系數（Kendallrank-order correlation coefficient，KROCC），皮爾森線性相關系數（Pearsonlinear correlation coefficient，PLCC）。客觀算法的結果和主觀評價的結果相關性越高，則以上三個系數的值越接近於1，說明算法越准確。由表可見，FSIM算法的准確度相對來說是最高的，三個系數的取值分分別達到了0.9094，0.7409，0.9050。
下表將上表的數值進行了一下排名。排在前面的有FSIM，IW-SSIM，RFSIM，MS-SSIM。猛然發現：PSNR真的是好不准啊~~
下表反映了每種全參考質量評價算法的耗時，耗時越短，說明算法速度越快。
總體說來FSIM，IW-SSIM，RFSIM這三種比較新的圖像質量評價算法准確性比較高。
原文鏈接：http://blog.csdn.net/leixiaohua1020/article/details/38324973
BD-Rate
BD-PSNR介紹
A、VCEG 建議采用Bjontegaard metric described in [1] to provide relative gain between two methods,by measuring average difference between the two RD-curves.
參考[3]中的文檔提供了這兩種值的計算工具：the bitrate saving between two methods, for a given objective quality; and the PSNR-Y delta for a given bitrate .
由此可見 Bjøntegaard delta bit rate (BDBR) 表示了在同樣的客觀質量下，兩種方法的碼率節省情況(Rate/distortion curves 畫一條水平線)
Bjøntegaard delta peak signal-to-noise rate (BD-PSNR)表示了在給定的同等碼率下，兩種方法的PSNR-Y的差異(Rate/distortion curves 畫一條垂直線)。
B、在HEVC中，[4]提供另外一種使用五點來計算BD-PSNR/Rate的工具，
參考：
[1] G. Bjøntegaard, “Calculation of average PSNR differences between RD-Curves,” ITU-T SG16 Q.6 Document, VCEG-M33,Austin,US, April 2001
[2] G. Bjøntegaard, “Improvements of the BD-PSNR model,” ITU-T SG16 Q.6 Document, VCEG-AI11,Berlin,Germany, July 2008
[3] An excel add-in for computing Bjontegaard metric and its evolution,VCEG-AE07
[4] BD-PSNR/Rate computation tool for five data points,JCTVC-B055
[5] S. Pateux, Tools for proposal evaluations, JCTVC-A031, April 2010.
