TAMPERE IMAGE DATABASE 2008 TID2008, version 1.0


TID2008 is intended for evaluation of full-reference image visual quality assessment metrics. TID2008 allows estimating how a given metric corresponds to mean human perception. For example, in accordance with TID2008, Spearman correlation between the metric PSNR (Peak Signal to Noise Ratio) and mean human perception (MOS, Mean Opinion Score) is 0.525.

How to test your metric? Step 1. Download the TID2008. Step 2. Calculate values of your metric for each distorted image of TID2008. Step 3. Use included software ("spearman.exe" or "kendall.exe") for calculate a correlation between your metric and MOS.

In case of publishing results obtained by means of TID2008 please refer to one of the following papers:

(full description of TID2008)
[1] N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, F. Battisti, "TID2008 - A Database for Evaluation of Full-Reference Visual Quality Assessment Metrics", Advances of Modern Radioelectronics, Vol. 10, pp. 30-45, 2009.

(a short description of TID2008)
[2] N. Ponomarenko, F. Battisti, K. Egiazarian, J. Astola, V. Lukin "Metrics performance comparison for color image database", Fourth international workshop on video processing and quality metrics for consumer electronics, Scottsdale, Arizona, USA. Jan. 14-16, 2009, 6 p.

WinRAR archive with TID2008 (~550 MB):

tid2008.rar



The TID2008 contains 25 reference images and 1700 distorted images (25 reference images x 17 types of distortions x 4 levels of distortions). Reference images are obtained by cropping from Kodak Lossless True Color Image Suite. All images are saved in database in Bitmap format without any compression. File names are organized in such a manner that they indicate a number of the reference image, then a number of distortion's type, and, finally, a number of distortion's level: "iXX_YY_Z.bmp".

The file "mos.txt" contains the Mean Opinion Score for each distorted image. The MOS was obtained from the results of 838 experiments carried out by observers from three countries: Finland, Italy, and Ukraine (251 experiments have been carried out in Finland, 150 in Italy, and 437 in Ukraine). Totally, the 838 observers have performed 256428 comparisons of visual quality of distorted images or 512856 evaluations of relative visual quality in image pairs. Higer value of MOS (0 - minimal, 9 - maximal, MSE of each score is 0.019) corresponds to higer visual quality of the image.


Ranking of compared metrics in accordance   Ranking of compared metrics in accordance
with Spearman correlation with MOS          with Kendall correlation with MOS        
Rank   Measure    Spearman correlation      Rank   Measure    Kendall correlation    
 1      MSSIM           0.853                1      MSSIM           0.654            
 2      SSIM            0.808                2      SSIM            0.605
 3      VIF             0.750                3      VIF             0.586            
 4      VSNR            0.705                4      VSNR            0.534            
 5      VIFP            0.655                5      VIFP            0.495            
 6      NQM             0.624                6      PSNR-HVS        0.476            
 7      UQI             0.600                7      NQM             0.461            
 8      PSNR-HVS        0.594                8      PSNR-HVS-M      0.449            
 9      XYZ             0.577                9      UQI             0.435            
10      IFC             0.569               10      XYZ             0.434            
11      PSNR-HVS-M      0.559               11      IFC             0.426            
12      PSNRY           0.553               12      PSNRY           0.402            
13      PSNR            0.525               13      WSNR            0.393            
14      MSE             0.525               14      LINLAB          0.381            
15      SNR             0.523               15      SNR             0.374            
16      WSNR            0.488               16      DCTUNE          0.372            
17      LINLAB          0.487               17      PSNR            0.369            
18      DCTUNE          0.476               18      MSE             0.369            

Types of distortion used in TID2008

N       Type of distortion        

1       Additive Gaussian noise
2       Additive noise in color components is more intensive than additive noise in the luminance component
3       Spatially correlated noise
4       Masked noise
5       High frequency noise
6       Impulse noise
7       Quantization noise
8       Gaussian blur
9       Image denoising
10      JPEG compression
11      JPEG2000 compression
12      JPEG transmission errors
13      JPEG2000 transmission errors
14      Non eccentricity pattern noise
15      Local block-wise distortions of different intensity
16      Mean shift (intensity shift)
17      Contrast change


The following files contain values of some quality metrics calculated for the TID2008 images:

"psnr.txt" - peak signal to noise ratio;
"psnry.txt" - peak signal to noise ratio calculated for the luminance component;
"snr.txt" - signal to noise ratio [3].
"mse.txt" - inverted values of mean square error [3].
"dctune.txt" - inverted values of the DCTune metric [4];
"uqi.txt" - values of the UQI metric [5];
"ssim.txt" - values of the SSIM metric [6];
"mssim.txt" - vaules of the MSSIM metric [7,3];
"linlab.txt" - inverted values of the LinLab metric [8];
"xyz" - inverted values of the YCxCz2XYZ metric [9];
"psnrhvs.txt" - values of the PSNR-HVS metric [10];
"psnrhvsm.txt" - values of the PSNR-HVS-M metric [11];
"vif.txt" - values of the VIF metric [12,3];
"vifp.txt" - pixel domain version VIF [12,3];
"nqm.txt" - values of the NQM metric [13,3];
"wsnr.txt" - values of the WSNR metric [14,3];
"ifc.txt" - values of the IFC metric [15,3];
"vsnr.txt" - values of the VSNR metric [16,3];

[3] Matthew Gaubatz, "Metrix MUX Visual Quality Assessment Package: MSE, PSNR, SSIM, MSSIM, VSNR, VIF, VIFP, UQI, IFC, NQM, WSNR, SNR", http://foulard.ece.cornell.edu/gaubatz/metrix_mux/
[4] A. B. Watson, "DCTune: A technique for visual optimization of DCT quantization matrices for individual images," Soc. Inf. Display Dig. Tech. Papers, vol. XXIV, pp. 946-949, 1993.
[5] Z. Wang, A. Bovik, "A universal image quality index", IEEE Signal Processing Letters, vol. 9, pp. 81-84, March, 2002.
[6] Z. Wang, A. Bovik, H. Sheikh, E. Simoncelli, "Image quality assessment: from error visibility to structural similarity", IEEE Transactions on Image Proc., vol. 13, issue 4, pp. 600-612, April, 2004.
[7] Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," Invited Paper, IEEE Asilomar Conference on Signals, Systems and Computers, Nov. 2003.
[8] B. Kolpatzik and C. Bouman, "Optimized Error Diffusion for High Quality Image Display", Journal Electronic Imaging, pp. 277-292, 1992.
[9] B. W. Kolpatzik and C. A. Bouman, "Optimized Universal Color Palette Design for Error Diffusion", Journal Electronic Imaging, vol. 4, pp. 131-143, 1995.
[10] K. Egiazarian, J. Astola, N. Ponomarenko, V. Lukin, F. Battisti, M. Carli, "New full-reference quality metrics based on HVS", CD-ROM Proceedings of the Second International Workshop on Video Processing and Quality Metrics, Scottsdale, USA, 2006, 4 p.
[11] N. Ponomarenko, F. Silvestri, K. Egiazarian, M. Carli, J. Astola, V. Lukin "On between-coefficient contrast masking of DCT basis functions", CD-ROM Proc. of the Third International Workshop on Video Processing and Quality Metrics. - USA, 2007. - 4 p.
[12] H.R. Sheikh.and A.C. Bovik, "Image information and visual quality," IEEE Transactions on Image Processing, Vol.15, no.2, 2006, pp. 430-444. [13] Damera-Venkata N., Kite T., Geisler W., Evans B. and Bovik A. "Image Quality Assessment Based on a Degradation Model", IEEE Trans. on Image Processing, Vol. 9, 2000, pp. 636-650.
[14] T. Mitsa and K. Varkur, "Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms", ICASSP '93-V, pp. 301-304.
[15] H.R. Sheikh, A.C. Bovik and G. de Veciana, "An information fidelity criterion for image quality assessment using natural scene statistics", IEEE Transactions on Image Processing, vol.14, no.12, 2005, pp. 2117-2128.
[16] D.M. Chandler, S.S. Hemami, "VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images", IEEE Transactions on Image Processing, Vol. 16 (9), pp. 2284-2298, 2007.



The programs "spearman.exe" and "kendall.exe" calculate values of Spearman and Kendall rank correlations for entire set of the TID2008 images as well as for particular subsets given in the following Table:
Subsets of TID2008 definded by default

N  Type of distortion     Noise Noise2 Safe Hard Simple Exotic Exotic2 Full

1  Additive Gaussian noise  +     +     +    -     +      -       -     +
2  Noise in color comp.     -     +     -    -     -      -       -     +
3  Spatially correl. noise  +     +     +    +     -      -       -     +
4  Masked noise             -     +     -    +     -      -       -     +
5  High frequency noise     +     +     +    -     -      -       -     +
6  Impulse noise            +     +     +    -     -      -       -     +
7  Quantization noise       +     +     -    +     -      -       -     +
8  Gaussian blur            +     +     +    +     +      -       -     +
9  Image denoising          +     -     -    +     -      -       -     +
10 JPEG compression         -     -     +    -     +      -       -     +
11 JPEG2000 compression     -     -     +    -     +      -       -     +
12 JPEG transm. errors      -     -     -    +     -      -       +     +
13 JPEG2000 transm. errors  -     -     -    +     -      -       +     +
14 Non ecc. patt. noise     -     -     -    +     -      +       +     +
15 Local block-wise dist.   -     -     -    -     -      +       +     +
16 Mean shift               -     -     -    -     -      +       +     +
17 Contrast change          -     -     -    -     -      +       +     +

The command line is "spearman data1 data2" or "kendall data1 data2".

Command line examples:

spearman mos.txt ssim.txt
kendall mos.txt dctune.txt
spearman linlab.txt xyz.txt
kendall psnr.txt psnr-hvs.txt

An example of usage:

kendall.exe mos.txt uqi.txt
Noise  : 0.363
Noise2 : 0.419
Safe   : 0.454
Hard   : 0.568
Simple : 0.586
Exotic : 0.214
Exotic2: 0.405
Full   : 0.438
We plan to regularly update the versions of this database. New versions will include new types of distortion and take into account results of additional experiments.

We will highly appreciate authors of other metrics if they will inform us (please, mail to karen@cs.tut.fi or nikolay@ponomarenko.info) how to get executable files (e.g., Matlab codes) of their metrics. We guarantee that we will not pass them to other users and will include future results obtained for such metrics in analysis for our database.

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Last changed: 2010-02-22.