Quality Assessment for Contrast-Distorted Images Based on Convolutional Neural Network

LIU Chun; TAN Kun; LIU Shao-hui; MA Ying-rui

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Quality Assessment for Contrast-Distorted Images Based on Convolutional Neural Network

更新时间：2026-09-04

- Quality Assessment for Contrast-Distorted Images Based on Convolutional Neural Network
- Vol. 35, Issue 4, Pages: 84-88(2018)
- 作者机构：
  
  1. 大庆师范学院机构计算机科学与信息技术学院,黑龙江,大庆,163712
  2. 哈尔滨工业大学计算机科学与技术学院,黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：
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- Published：2018
- 稿件说明：
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刘春, 谭琨, 刘绍辉, 马英瑞. 基于卷积神经网络的对比度失真图像质量评价[J]. 微电子学与计算机, 2018, 35(4): 84-88.

LIU Chun, TAN Kun, LIU Shao-hui, et al. Quality Assessment for Contrast-Distorted Images Based on Convolutional Neural Network[J]. 2018, 35(4): 84-88.
刘春, 谭琨, 刘绍辉, 马英瑞. 基于卷积神经网络的对比度失真图像质量评价[J]. 微电子学与计算机, 2018, 35(4): 84-88. DOI：

LIU Chun, TAN Kun, LIU Shao-hui, et al. Quality Assessment for Contrast-Distorted Images Based on Convolutional Neural Network[J]. 2018, 35(4): 84-88. DOI：

摘要

研究对比度失真的图像质量评价方法.借助卷积神经网络来设计图像质量的评价算法.在提出的算法中

设计了一个多层的卷积神经网络

包括三层卷积层、三层池化层和三层全连接层

该网络可以自动地学习与图像质量相关的特征.利用现有的数据库对设计的网络进行训练

使其可以对图像的质量做出预测.通过实验测试

证明了提出的方法可以取得较高的预测性能

以及超过了主流的图像质量评价模型.

Abstract

In this paper

we devise a dedicated quality evaluation scheme to automatically predict the quality of contrast-changed images

which is based on the newly presented convolutional neural network (CNN) as CNN has been proved quite effective in dealing with computer vision tasks. The designed CNN includes three convolution layers and two fully connected layers. By training the CNN on existing image quality databases

we can evaluate the quality of the contrast-distorted images. Experimental results demonstrate the proposed method delivers high prediction performance and outperform mainstream image quality methods.

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Keywords

references

Mocanu D C, Exarchakos G, Liotta A. Deep learning for objective quality assessment of 3D images[C]//2014 IEEE International Conference on Image Processing (ICIP). Paris: IEEE, 2014: 758-762.

Gu K, Zhai G, Yang X. Subjective and objective quality assessment for images with contrast change[C]//Proc 20th IEEE Int Conf Image Process(ICIP). IEEE, 2013: 383-387.

Guan J, Yi S, Zeng X, et al. Visual importance and distortion guided deep image quality assessment framework[J]. IEEE Transactions on Multimedia, 2017, 19(11): 2505-2520. DOI: 2505

Soundararajan R, Bovik A C, Rred indices: reduced reference entropic differencing for image quality assessment[J]. IEEE Trans Image Process, 2012, 21 (2): 517-526. DOI: 517

Hou W, Gao X, Tao D, et al. Blind image quality assessment via deep learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(6): 1275-1286. DOI: 1275

Zhai G, Wu X, Yang X, et al. A psychovisual quality metric in freeenergy principle[J]. IEEE Trans Image Process, 2012, 21 (1) : 41-52. DOI: 41

Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13 (4): 600-612. DOI: 600

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