Eric C. Larson
Associate ProfessorComputer Science
Bobby B. Lyle School of Engineering
Southern Methodist University
twitter: @ec_larson
email: eclarson@lyle.smu.edu
CS Office: 451 Caruth Hall
Lyle School of Engineering
Caruth Hall
3145 Dyer Street, Suite 445
Dallas, TX 75205
SMU UbiComp Lab:
Johnson Square 189
Categorical Subjective Image Quality (CSIQ)
I made this dataset while working at the The Image Coding and Analysis Lab at Oklahoma State University. The CSIQ database consists of 30 original images, each is distorted using six different types of distortions at four to five different levels of distortion. CSIQ images are subjectively rated base on a linear displacement of the images across four calibrated LCD monitors placed side by side with equal viewing distance to the observer. The database contains 5000 subjective ratings from 35 different observers, and ratings are reported in the form of DMOS.
The files can be downloaded in multiple parts:
When using the Database, please cite the following:
I made this dataset while working at the The Image Coding and Analysis Lab at Oklahoma State University. The CSIQ database consists of 30 original images, each is distorted using six different types of distortions at four to five different levels of distortion. CSIQ images are subjectively rated base on a linear displacement of the images across four calibrated LCD monitors placed side by side with equal viewing distance to the observer. The database contains 5000 subjective ratings from 35 different observers, and ratings are reported in the form of DMOS.
The files can be downloaded in multiple parts:
- DMOS Scores (excel file <1MB): csiq.DMOS.xls
- Source Images (~14 MB): src_imgs.zip
- Distorted Images (~375 MB): dst_imgs.zip
When using the Database, please cite the following:
- E. C. Larson and D. M. Chandler, "Most apparent distortion: full-reference image quality assessment and the role of strategy," Journal of Electronic Imaging, 19 (1), March 2010. Full Text PDF
- Source Code.
- Ding, K., Ma, K., Wang, S. et al. Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems. Int J Comput Vis 129, 1258–1281 (2021). DOI
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