On color image quality assessment using natural image statistics
This work addresses color distortion issues in image quality assessment for applications like image processing and multimedia, but it is incremental as it extends existing methods to color spaces.
The paper tackled the problem of color image quality assessment by extending grayscale image-statistics based measures to color images, finding that using the CIELAB color representation significantly improves performance for many distortion types on the TID 2013 benchmark.
Color distortion can introduce a significant damage in visual quality perception, however, most of existing reduced-reference quality measures are designed for grayscale images. In this paper, we consider a basic extension of well-known image-statistics based quality assessment measures to color images. In order to evaluate the impact of color information on the measures efficiency, two color spaces are investigated: RGB and CIELAB. Results of an extensive evaluation using TID 2013 benchmark demonstrates that significant improvement can be achieved for a great number of distortion type when the CIELAB color representation is used.