Image Quality Assessment and Color Difference
This work addresses image quality assessment for applications like photography and display systems, but it is incremental as it builds on existing color difference formulas and benchmarks.
The authors tackled the problem of image quality assessment by combining pixel-wise, structural, and color-based metrics, extending the CIEDE2000 formula with perceptual color difference. The result was a new metric (PCDM) that achieved linear correlations of up to 97.9% on noise and compression artifacts in the LIVE database, with an overall correlation of 92.7%, and it captured color-based artifacts missed by structure-based methods.
An average healthy person does not perceive the world in just black and white. Moreover, the perceived world is not composed of pixels and through vision humans perceive structures. However, the acquisition and display systems discretize the world. Therefore, we need to consider pixels, structures and colors to model the quality of experience. Quality assessment methods use the pixel-wise and structural metrics whereas color science approaches use the patch-based color differences. In this work, we combine these approaches by extending CIEDE2000 formula with perceptual color difference to assess image quality. We examine how perceptual color difference-based metric (PCDM) performs compared to PSNR, CIEDE2000, SSIM, MS-SSIM and CW-SSIM on the LIVE database. In terms of linear correlation, PCDM obtains compatible results under white noise (97.9%), Jpeg (95.9%) and Jp2k (95.6%) with an overall correlation of 92.7%. We also show that PCDM captures color-based artifacts that can not be captured by structure-based metrics.