Computational Evaluation Methods of Visual Complexity Perception for Images

doi:10.3969/j.issn.0372-2112.2020.04.024

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Computational Evaluation Methods of Visual Complexity Perception for Images

更新时间：2025-07-08

- Computational Evaluation Methods of Visual Complexity Perception for Images
- Acta Electronica Sinica Vol. 48, Issue 4, Pages: 819-826(2020)
- 作者机构：
  
  1. 山西大学软件学院,山西,太原,030013
  2. 浙江理工大学信息学院,浙江,杭州,310018
  3. 山西大学大数据科学与产业研究院, 山西大学计算智能与中文信息处理教育部重点实验室,山西,太原,030006
  4. 山西大学软件学院,山西,太原,030013
  5. 浙江理工大学信息学院,浙江,杭州,310018
  6. 山西大学大数据科学与产业研究院山西大学计算智能与中文信息处理教育部重点实验室,山西,太原,030006
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2020.04.024
  CLC： TP391
- Published Online：25 April 2020，
  
  Published：2020
- 稿件说明：
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Computational Evaluation Methods of Visual Complexity Perception for Images[J]. Acta Electronica Sinica, 2020, 48(4): 819-826.
DOI：

Computational Evaluation Methods of Visual Complexity Perception for Images[J]. Acta Electronica Sinica, 2020, 48(4): 819-826. DOI： 10.3969/j.issn.0372-2112.2020.04.024.

摘要

可计算的图像复杂度评价是让计算机模拟人类视觉感知，从而对图像视觉复杂度进行决策的研究，该研究属于多学科交叉的创新性研究课题，在图像工程领域具有重要意义.本文针对可计算的图像复杂度评价方法进行了全面的梳理和分析，文中首先回顾了图像复杂度的应用领域，并详细阐述了图像复杂度评价方法，从信息论、图像压缩理论、图像特征分析、眼动数据等方面进行总结；随后，着重阐述基于图像特征的图像复杂度评价方法中所使用的图像特征；归纳图像复杂度建模中的分类和回归问题；最后，总结当前图像视觉复杂度评价方法存在的问题和挑战，展望图像复杂度的计算化发展方向.

Abstract

Modeling of image complexity is a research that builds a computer to sense the visual complexity of images in a way that is similar to the majority of people. The research is an interdisciplinary subject and is of great significance in the field of image engineering. In this article

the main methods of evaluating image complexity have been carried out by a comprehensive analysis. The paper reviews the different application fields of image complexity. Then the paper introduces the evaluation methods for visual complexity in detail from information theory

theory of image compression

image analysis and image modeling method of complexity. Especially

we focus on descriptions of the image features used in the complexity evaluation method based on the image features. In terms of image complexity assessment

we illustrate the classification and regression methods for evaluating complexity of images. This work also proposes the existing problems and challenges in the study of image complexity.

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