App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 9

Weighted polynomial regression color characterization algorithm with residual correction

Yang Chen1
Lian Kaicheng1
Xu Hao1
Wu Qin1,2
Chai Zhilei1,2
1. School of Artificial Intelligence & Computer Science, Jiangnan University, Wuxi Jiangsu 214122, China
2. Jiangsu Provincial Engineering Laboratory of Pattern Recongnition & Computational Intelligence, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0597

Abstract

In the field of digital printing, accurately reproducing the color of computer images is a prerequisite for high-quality printing, where color characterization is a key step. Traditional polynomial regression models tend to amplify the outliers in the characterization sample set due to high-order terms, causing model oscillation and affecting the accuracy of color characterization. Color characterization algorithms based on neural network have higher precision but significantly increase in algorithmic complexity, making them unsuitable for the efficiency requirements in industrial production. To address these issues, this paper proposed a color characterization method based on weighted polynomial regression algorithm with residual correction. This algorithm employed the Huber loss function, known for its strong robustness against outliers, as a substitute for mean squared error. It determined the weight of each sample through an adaptive mechanism and iteratively optimizes the residual values to obtain the optimal weight matrix, thus mitigating the impact of outlier samples on the system. Additionally, the correction module captured nonlinear scenarios that the initial model may miss, assisting the adjustment of the transformation results and thereby enhanced characterization precision. The results show that compared to conventional polynomial regression, this algorithm reduces the average color difference by 1.2. It achieves a precision close to that of deep belief network algorithms but with more than 91.5% reduction in inference time.

Foundation Support

国家自然科学基金资助项目(61972180)
江苏省模式识别与计算智能工程实验室项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0597
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 9

Publish History

[2024-02-27] Accepted Paper

Cite This Article

杨晨, 廉凯成, 徐昊, 等. 残差修正的加权多项式回归色彩特征化算法 [J]. 计算机应用研究, 2024, 41 (9). (2024-05-14). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0597. (Yang Chen, Lian Kaicheng, Xu Hao, et al. Weighted polynomial regression color characterization algorithm with residual correction [J]. Application Research of Computers, 2024, 41 (9). (2024-05-14). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0597. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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