A novel image quality assessment based on an adaptive feature for image characteristics and distortion types
Sung‐Ho Bae, Munchurl Kim · 2015
In this paper, we reveal that many conventional features used in computational image quality assessment (IQA) methods can hardly characterize perceived distortions on various image characteristics and distortion types, thus resulting in relatively low prediction performance of visual quality scores. To solve this problem, we propose a new IQA method, called Structural Contrast-Quality Index (SC-QI) which is based on structural contrast index (SCI) as a very effective feature. SCI can adaptively quantify perceived distortions depending on various image characteristics and distortions types. In addition to SCI, some other perceptually important features that reflect effects of contrast sensitivity function and chrominance component variation are also combined into the proposed SC-QI. Our comprehensive experiments on three large IQA datasets verify that the proposed SC-QI outperforms the state-of-the-art ones while accompanying lower computational complexity.