Adaptive color optimization algorithm of image engine in display system

ZHANG Bo-wen; XIA Zhen-ping; ZHOU Bo; SONG Yu; MA Feng-yun; BAI Yi

doi:10.37188/CJLCD.2022-0143

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Adaptive color optimization algorithm of image engine in display system

Image Processing | 更新时间：2022-10-28

- Adaptive color optimization algorithm of image engine in display system
- Chinese Journal of Liquid Crystals and Displays Vol. 37, Issue 11, Pages: 1467-1475(2022)
- 作者机构：
  
  苏州科技大学电子与信息工程学院，江苏苏州 215009
- 作者简介：
- 基金信息：
  
  Supported by National Natural Science Foundation of China(62002254;61876121);Natural Science Foundation of Jiangsu Province, China(BK20200988);Postgraduate Research and Practice Innovation Project of Jiangsu Province （No.SJCX21_1424, No.KYCX20_2755）
- DOI：10.37188/CJLCD.2022-0143
  CLC： TP391.9
- Received：23 April 2022，
  
  Revised：07 May 2022，
  
  Published：05 November 2022
- 稿件说明：
移动端阅览
ZHANG Bo-wen, XIA Zhen-ping, ZHOU Bo, et al. Adaptive color optimization algorithm of image engine in display system[J]. Chinese journal of liquid crystals and displays, 2022, 37(11): 1467-1475.
DOI：

ZHANG Bo-wen, XIA Zhen-ping, ZHOU Bo, et al. Adaptive color optimization algorithm of image engine in display system[J]. Chinese journal of liquid crystals and displays, 2022, 37(11): 1467-1475. DOI： 10.37188/CJLCD.2022-0143.

摘要

图像引擎通过多种特定的算法对图像信号进行优化，在显示系统中具有极其重要的作用和地位。图像引擎中的传统色彩优化算法由多种图像算法组合而成，存在无法进行自适应优化、容易放大图像噪声等问题。为此，提出了一种基于空洞卷积的全卷积神经网络用于构建优化算法，侧重于从人眼主观感知的角度优化图像色彩。同时，有针对性地构建大规模数据集以提高算法的泛化能力，防止过拟合。测试结果表明，所提出的算法可以有效地增强原始图像的色彩，相较于传统方法，平均峰值信噪比提升了4.01 dB，平均结构相似性提升了0.04。主观对比实验结果表明，提出的算法对图像主观感知质量有显著性影响，平均提升了61%。

Abstract

Image engine optimizes the image signal through a variety of specific algorithms， which plays an extremely important role in the display system. The traditional color optimization algorithm of image engine is composed of various image optimization algorithms， which can not optimize images adaptively and easy to amplifies the noise. Therefore， a full convolution neural network based on dilated convolution is proposed to construct the optimization algorithm， which focuses on optimizing images from the perspective of subjective perception. At the same time， a large-scale dataset is constructed to improve the generalization ability of the algorithm and prevent overfitting. The experiment results show that the proposed algorithm can effectively enhance the color of original images. Compared with the traditional method， the average peak signal-to-noise ratio is improved by 4.01 dB and the average structural similarity is improved by 0.04. The subjective comparison experiment shows that the proposed algorithm has a significant impact on the subjective perception quality of the image， with an average improvement of 61%.

关键词

Keywords

references

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