In this letter, we estimate perceived image quality using sparse representations obtained from generic image databases through an unsupervised learning approach. A color space transformation, a mean subtraction, and a whitening operation are used to enhance descriptiveness of images by reducing spatial redundancy; a linear decoder is used to obtain sparse representations; and a thresholding stage is used to formulate suppression mechanisms in a visual system. A linear decoder is trained with 7 GB worth of data, which corresponds to 100 000 8 × 8 image patches randomly obtained from nearly 1000 images in the ImageNet 2013 database. A patch-wise training approach is preferred to maintain local information. The proposed quality estimator UNIQUE is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases and compared with 13 quality estimators. Experimental results show that UNIQUE is generally a top performing quality estimator in terms of accuracy, consistency, linearity, and monotonic behavior.
- D. Temel, M. Prabhushankar, and G. AlRegib, “UNIQUE: Unsupervised Image Quality Estimation,” in IEEE Signal Processing Letters , vol.23, no.10, pp.1414-1418, Oct. 2016. [PDF] [Poster/Lec] [Bib] [Code]
- M. Prabhushankar, D. Temel and G. AlRegib, “MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation,” to be presented at Image Quality and System Performance XIV, part of IS&T Electronic Imaging , San Francisco, CA, Jan. 29 – Feb. 2, 2017. [PDF] [Poster/Lec] [Bib] [Code]