Abstract: An image quality evaluation method based on improved Weber local features is proposed. Firstly, the mechanism of image contrast recognition by human visual system is simulated. The improved gray optimization algorithm retains the best contrast of color image. Secondly, prewitt operator is used to calculate the gradient direction in the neighborhood. Calculate and sum the differential excitation values in the vertical and horizontal directions in the neighborhood to obtain the edge information of the image. Finally, use the support vector machine to train the one-dimensional feature data of the images in various databases to construct image quality evaluation model. Experiments show that the method has the advantages of higher accuracy, better applicability and strong prediction direction.

Key words: human visual system, image quality assessment, Weber local descriptor