The conventional rate-distortion image compression framework has been developed for more than 30 years. Although the peak-signal-to-noise-ratio (PSNR) index has been widely used to measure the quality of compressed image/video, it does not take the Human Visual System (HVS) characteristics into consideration. To address this challenge, it is essential to explore new perceptual quality indices. In this project, we adopt a statistical approach to study the perceptual quality indices and propose a new methodology to characterize the human visual experience on coded images based on the notion of just-noticeable difference (JND). We built a large-scale compressed image dataset of a wide variety of image content and invited a large number of subjects to participate in the subjective test.
Contact Info: haiqianw AT usc.edu
If you use this dataset in your work, please cite the following three related papers:
Detailed description of the MCL-JCI
 Lina Jin, Joe Yuchieh Lin, Sudeng Hu, Haiqiang Wang, Ping Wang, Ioannis Katsavounidis, Anne Aaron and C.-C. Jay Kuo. “Statistical Study on Perceived JPEG Image Quality via MCL-JCI Dataset Construction and Analysis.” Electronic Imaging (2016), the Society for Imaging Science and Technology (IS&T). PDF
JND data processing
 Sudeng Hu, Haiqiang Wang and C.-C. Jay Kuo, “A GMM-based stair quality model for human perceived JPEG images,” IEEE International Conference on Acoustic, Speech and Signal Processing, Shanghai, China, March 20-25, 2016.. PDF
Overview on JND-based quality measure of coded image/video
 Joe Yuchieh Lin, Lina Jin, Sudeng Hu, Ioannis Katsavounidis, Anne Aaron and C.-C. Jay Kuo. “Experimental Design and Analysis of JND Test on Coded Image/Video.” SPIE Optical Engineering+ Applications. International Society for Optics and Photonics, 2015 . PDF
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