LIVE Image Quality Assessment Database

Author: LIVE - University of Texas

Partner: No

Contact: Anush Moorthy (




Subjective scores: true

Total: 800

SRC: 29

HRC: 27

Ratings: 32

Resolution: 768x512

Method: ACR


A typical example of image quality database is the one available from the Laboratory for Image & Video Engineering (LIVE) from the University of Texas at Austin, which has been used in many studies. It is popular among researchers. This LIVE Image Quality Assessment Database (LIVE IQAD) contains still images annotated with MOS ratings. The Release 2 distortions include JPEG (169 images), JPEG2000 (175 images), white noise (145 images), Gaussian blur (145 images) and JPEG2000 with bit errors in simulated Rayleigh fading channel (145 images).


ZIP archive (~700MB), password protected, registration form to be filled. Link:


Link: Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirety appear in all copies of this database, and the original source of this database, Laboratory for Image and Video Engineering (LIVE, and Center for Perceptual Systems (CPS, at the University of Texas at Austin (UT Austin,, is acknowledged in any publication that reports research using this database.

References and Citation

We have decided to make the data set available to the research community free of charge. If you use these images in your research, we kindly ask that you reference this website and our papers listed below [SSB06], [WBS04], [SWL04].


  • SSB06: H.R. Sheikh, M.F. Sabir and A.C. Bovik, "A statistical evaluation of recent full reference image quality assessment algorithms", IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3440-3451, Nov. 2006.
  • SWL04: H.R. Sheikh, Z.Wang, L. Cormack and A.C. Bovik, "LIVE Image Quality Assessment Database Release 2",
  • WBS04: Z. Wang, A.C. Bovik, H.R. Sheikh and E.P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing , vol.13, no.4, pp. 600- 612, April 2004.