Towards no-reference image quality assessment based on multi-scale convolutional neural network

被引：0
|
作者：
Ma, Yao [1 ]
Cai, Xibiao [1 ]
Sun, Fuming [2 ]
机构：
[1] School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou,121001, China
[2] School of Information and Communication Engineering, Dalian Minzu University, Dalian,116600, China
来源：
CMES - Computer Modeling in Engineering and Sciences | 2020年 / 123卷 / 01期
关键词：
Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems. Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information. Actually; the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image. In light of this; we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network; which integrates both global information and local information of an image. We first adopt the image pyramid method to generate four scale images required for network input and then provide two network models by respectively using two fusion strategies to evaluate image quality. In order to better adapt to the quality assessment of the entire image; we use two different loss functions in the training and validation phases. The superiority of the proposed method is verified by several different experiments on the LIVE datasets and TID2008 datasets. © 2020 Tech Science Press. All rights reserved;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：201 / 216
相关论文
共 50 条
  • [31] No-Reference Image Quality Assessment Using Independent Component Analysis and Convolutional Neural Network
    Chuang Zhang
    Jiawei Xu
    Xiaoyu Huang
    Seop Hyeong Park
    Journal of Electrical Engineering & Technology, 2019, 14 : 487 - 496
  • [32] No-Reference Image Quality Assessment Using Independent Component Analysis and Convolutional Neural Network
    Zhang, Chuang
    Xu, Jiawei
    Huang, Xiaoyu
    Park, Seop Hyeong
    JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2019, 14 (01) : 487 - 496
  • [33] A Novel Patch Variance Biased Convolutional Neural Network for No-Reference Image Quality Assessment
    Po, Lai-Man
    Liu, Mengyang
    Yuen, Wilson Y. F.
    Li, Yuming
    Xu, Xuyuan
    Zhou, Chang
    Wong, Peter H. W.
    Lau, Kin Wai
    Luk, Hon-Tung
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 29 (04) : 1223 - 1229
  • [34] No-Reference Quality Assessment Based on Dual-Channel Convolutional Neural Network for Underwater Image Enhancement
    Hu, Renzhi
    Luo, Ting
    Jiang, Guowei
    Lin, Zhiqiang
    He, Zhouyan
    ELECTRONICS, 2024, 13 (22)
  • [35] No-reference Image Blur Assessment Based on Multi-scale Spatial Local Features
    Sun, Chenchen
    Cui, Ziguan
    Gan, Zongliang
    Liu, Feng
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2020, 14 (10): : 4060 - 4079
  • [36] No-reference image quality assessment with multi-scale weighted residuals and channel attention mechanism
    Wang, Changzhong
    Lv, Xiang
    Ding, Weiping
    Fan, Xiaodong
    SOFT COMPUTING, 2022, 26 (24) : 13449 - 13465
  • [37] No-Reference Image Quality Assessment via Local and Global Multi-Scale Feature Integration
    Xu, Kepeng
    He, Gang
    Qiao, Tong
    Liu, Zhenyang
    PROCEEDINGS OF THE 3RD WORKSHOP ON QUALITY OF EXPERIENCE IN VISUAL MULTIMEDIA APPLICATIONS, QOEVMA 2024, 2024, : 21 - 29
  • [38] No-reference image quality assessment with multi-scale weighted residuals and channel attention mechanism
    Changzhong Wang
    Xiang Lv
    Weiping Ding
    Xiaodong Fan
    Soft Computing, 2022, 26 : 13449 - 13465
  • [39] Texture dominated no-reference quality assessment for high resolution image by multi-scale mechanism
    Huang, Ziqing
    Liu, Hao
    Jia, Zhihao
    Zhang, Shuo
    Zhang, Yonghua
    Liu, Shiguang
    NEUROCOMPUTING, 2025, 636
  • [40] No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
    Varga, Domonkos
    APPLIED SCIENCES-BASEL, 2022, 12 (01):
12345