No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics
By: Researcher II
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| Authors | Yuming Fang, Kede Ma, Zhou Wang, Weisi Lin, Zhijun Fang, and Guangtao Zhai |
| Journal/Conference Name | IEEE Signal Processing Letters |
| Paper Category | ECE |
| Paper Abstract | Contrast distortion is often a determining factor in human perception of image quality, but little investigation has been dedicated to quality assessment of contrast-distorted images without assuming the availability of a perfect-quality reference image. In this letter, we propose a simple but effective method for no-reference quality assessment of contrast distorted images based on the principle of natural scene statistics (NSS). A large scale image database is employed to build NSS models based on moment and entropy features. The quality of a contrast-distorted image is then evaluated based on its unnaturalness characterized by the degree of deviation from the NSS models. Support vector regression (SVR) is employed to predict human mean opinion score (MOS) from multiple NSS features as the input. Experiments based on three publicly available databases demonstrate the promising performance of the proposed method. |
| Date of publication | 2015 |
| Code Programming Language | MATLAB |
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