A Novel Image Quality Assessment With Globally and Locally Consilient Visual Quality Perception.
Computational models for image quality assessment (IQA) have been developed by exploring effective features that are consistent with the characteristics of a human visual system (HVS) for visual quality perception. In this paper, we first reveal that many existing features used in computational IQA methods can hardly characterize visual quality perception for local image characteristics and various distortion types. To solve this problem, we propose a new IQA method, called the structural contrast quality index (SC QI) by adopting a structural contrast index (SCI) which can well characterize local and global visual quality perceptions for various image characteristics with structural distortion types. In addition to SCI, we devise some other perceptually important features for our SC QI that can effectively reflect the characteristics of HVS for contrast sensitivity and chrominance component variation. Furthermore, we develop a modified SC QI, called structural contrast distortion metric (SC DM) which inherits desirable mathematical properties of valid distance metricability and quasi convexity. So, it can effectively be used as a distance metric for image quality optimization problems. Extensive experimental results show that both SC QI and SC DM can very well characterize the HVS's properties of visual quality perception for local image characteristics and various distortion types, which is a distinctive merit of our methods compared with other IQA methods. As a result, both SC QI and SC DM have better performances with a strong consilience of global and local visual quality perception as well as with much lower computation complexity, compared with the state of the art IQA methods. The MATLAB source codes of the proposed SC QI and SC DM are publicly available online at https:/sites.google.com/site/sunghobaecv/iqa.
IEEE transactions on image processing a publication of the IEEE Signal Processing Society