Exploring the Optimization Methods of Color Matching in Visual Communication Design Combined with Graphic Algorithms

In order to solve the problems of inefficiency of traditional color matching methods in visual communication design, this paper proposes a computer-supported color extraction and matching technology. Based on the improved K-Means clustering algorithm to achieve color adaptive extraction, the integration of visual perception and similarity metrics to complete the color matching evaluation. A sample database is constructed and students majoring in visual communication are selected as the survey object. The performance level of the color adaptive extraction algorithm in this paper is evaluated by comparing the similarity results of the reconstructed image and the original image. Combined with the perceptual evaluation survey data, with the help of factor analysis to mine the subjects' perceptual demand for color matching scheme, the color matching optimization scheme is proposed. The traditional color matching optimization method is chosen as a comparison to examine the superiority of the proposed method. At 500 iterations, the fusion degree of this paper's method reaches 93.59%, which is much higher than the 56.78%, 70.86% and 78.31% of other traditional methods. The output signal-to-noise ratio is 26.18 dB, which is 36.28% higher than the PID method with the second best performance. Meanwhile, the output rate of color matching visual reconstruction of this paper's method is stable between 85% and 92% under different iterations, with a fluctuation of no more than 10%.