Multiply Distorted Image DatabaseúĘMDIDúę
MDID is a database for evaluating the results of image quality assessment metrics on multiply distorted images. It consists of 20 reference images and 1600 distorted images.
The reference images are selected from several popular databases [22, 30-34], and are cropped into the size of 512 X 384 pixels without scaling or rotation.
The distorted images are obtained from degrading reference images with random types and levels of distortions. In this way, each distorted image contains multiple types of distortions simultaneously. Five distortions, namely Gaussian noise (GN), Gaussian blur (GB), contrast change (CC), JPEG and JPEG2000 are introduced:
Gaussian Noise: The MATLAB functioní«imnoiseí»is used to add Gaussian noise to reference images. Gaussian Blur: The MATLAB functionsí«fspecialí»andí«imfilterí»are used to blur reference images. Contrast Change: A segmented linear function is used to cause larger contrast in a certain range and smaller contrast in the other. JPEG: The MATLAB functioní«imwriteí»is used to compress reference images. JPEG2000: Kakadu tools  is used to compress reference images.According to the characteristics of image acquisition, transmission and display, we designed the order to introduce distortions as follows: GB or CC first, JPEG or JPEG2000 second and GN last. The process of obtaining distorted images of a reference image can be epitomized as follows:
(1).Generate a random 1í┴5 integer matrix M, its elements range from 0 to 4 and denote the distortion levels of corresponding five distortions. (2).Introduce distortions to the reference image with parameter M in the order mentioned above.All of these Ms are recorded in the files: í«1.txtí», í«2.txtí», í«3.txtí» and so on. To specify, í«1.txtí» contains the parameters Ms utilized to obtain the 80 distorted images corresponding to 1st reference image.
 E. C. Larson and D. M. Chandler, Most apparent distortion: Full-reference image quality assessment and the role of strategy, J. Electron. Imaging 19 (2010).  ImageNet: [Online]. Available: http://image-net.org/download-images  SIPI: The USC-SIPI Image Database, [Online]. Available: http://sipi.usc.edu/database/  A. Gallagher, T. Chen, Understanding Groups of Images of People, IEEE Conference on CVPR, 2009, pp. 256-263.  J. Philbin, O. Chum, M. Isard, J. Sivicand, and A. Zisserman, Object retrieval with large vocabularies and fast spatial matching, in: Proceedings of the IEEE Conference on CVPR, 2007, pp. 1-8.  R. C. Gonzalez, R. E. Woods, Didital Image Processing, Third edition, Beijing: Publishing House of Electronics Industry, 2011.  D. S. Taubman and M. W. Marcellin, JPEG2000: Image Compression Fundamentals, Standards, and Practice. Norwell, MA: Kluwer, 2001.
All rights of the MDID Database are
reserved. The database is only available for academic research and
noncommercial purposes. Any commercial uses of this database are strictly
prohibited. Please cite the following paper if you use this database in your research:
information: Fei Zhou, Postdoctoral Fellow Visual Information
Processing Lab. (VIP Lab.) Department of
Electronic Engineering/Graduate School at Shenzhen Tsinghua University Tsinghua campus,
the university town of Xili, Nanshan District, Shenzhen, China E-mail: firstname.lastname@example.org Update date: 2016-08-25
W. Sun, F. Zhou, Q. M. Liao. MDID: a multiply distorted image database for image quality assessment, Pattern Recognit. 61C (2017) pp. 153-168.
Fei Zhou, Postdoctoral Fellow
Visual Information Processing Lab. (VIP Lab.)
Department of Electronic Engineering/Graduate School at Shenzhen Tsinghua University
Tsinghua campus, the university town of Xili, Nanshan District, Shenzhen, China