Blind Image Quality Metric for Color Images Based on Human Vision System and Deep CNN

This article introduces a novel blind image quality metric (BIQM) for color images which is designed taking into account human visual system characteristics. The BIQM has a four-stage framework: RGB to YUV transformation, denoising with convolutional neural network , quality evaluation, and weighting to make it compatible with the human visual system. Experimental results, including Spearman's rank-order correlation coefficient, confirm BIQM's effectiveness, particularly in scenarios involving white noise and its compatibility with the human visual system. Furthermore, a survey involving 100 participants ranks images based on three distinct qualities, validating the method's alignment with the human visual system. The comparative analysis reveals that the proposed BIQM can compete with commonly used non-referenced quality measures and is more accurate than some of them. The MATLAB codes for the development of the BIQM are made available through the provided link: are available in the link: https://bit.ly/49MrbFX