[1]王伟,刘辉,杨俊安.一种特征字典映射的图像盲评价方法研究[J].智能系统学报,2018,13(06):989-993.[doi:10.11992/tis.201805027]
 WANG Wei,LIU Hui,YANG Junan.Blind quality evaluation with image features codebook mapping[J].CAAI Transactions on Intelligent Systems,2018,13(06):989-993.[doi:10.11992/tis.201805027]
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一种特征字典映射的图像盲评价方法研究(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年06期
页码:
989-993
栏目:
出版日期:
2018-10-25

文章信息/Info

Title:
Blind quality evaluation with image features codebook mapping
作者:
王伟12 刘辉2 杨俊安2
1. 军事科学院 评估论证研究中心, 北京 100091;
2. 国防科技大学 电子对抗学院, 安徽 合肥 230037
Author(s):
WANG Wei12 LIU Hui2 YANG Jun’an2
1. Center for Assessment and Demonstration Research, Academy of Military Science, Beijing 100091, China;
2. Institute of Electronic Warfare, National University of Defense Technology, Hefei 230037, China
关键词:
客观评价盲评价图像质量评价局部结构特征全局统计特性特征提取字典池化映射
Keywords:
objective assessmentblind assessmentimage quality assessmentlocal structural featureglobal statistics characteristicsfeature extractioncodebookpooling mapping
分类号:
TP391.4
DOI:
10.11992/tis.201805027
摘要:
图像质量评价对于许多计算机视觉任务来说,是至关重要的一环。传统的方法往往聚焦于人类直观打分,其最大不足就是打分数据的庞大性。为了解决这个难题,本文提出了一种图像质量盲评价框架。首先分别提取图像的局部结构特征和全局统计特性,在学习阶段,提出了一种基于字典池的映射策略来加速打分的进程。实验结果显示,本文所提方法准确度和鲁棒性相比较时下其他算法,取得了更加令人满意的结果。
Abstract:
Image quality assessment is crucial to many computer vision tasks. Traditional approaches concentrate on human perceptual scoring. The biggest hurdle to these subjective efforts is the difficulty of collecting the enormous human scored data. To solve this difficulty, we propose a blind image quality assessment framework. Starting with local structural characteristics and global statistics characteristics of images, we utilize a codebook-based pooling strategy to accelerate the scoring stage. Experimental results show that by comparison with other algorithm, an effective performance in accuracy and robustness was achieved using the proposed approach.

参考文献/References:

[1] WANG Wei, LIU Hui, ZHENG Jinjin, et al. Integrated blur image quality assessment based on human visual perception[C]//Proceedings of the International Conference on Computer Science and Applications. Wuhan, China, 2017:119-124.
[2] FERZLI R, KARAM L J. A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)[J]. IEEE transactions on image processing, 2009, 18(4):717-728.
[3] HASSEN R, WANG Zhou, SALAMA M. No-reference image sharpness assessment based on local phase coherence measurement[C]//Proceedings of 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. Dallas, USA, 2010:2434-2437.
[4] CHEN Mingjun, BOVIK A C. No-reference image blur assessment using multiscale gradient[C]//Proceedings of 2009 International Workshop on Quality of Multimedia Experience. San Diego, USA, 2009:3.
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[6] CIANCIO A, DA COSTA A L, DA SILVA E A, et al. No-reference blur assessment of digital pictures based on multifeature classifiers[J]. IEEE transactions on image processing, 2010, 20(1):64-75.
[7] YAO Hongxun, HUSEH M Y, YAO Guilin, et al. Image evaluation factors[C]//KAMEL M, CAMPILHO A. Image Analysis and Recognition. Berlin, Heidelberg:Springer, 2005:255-262.
[8] HASLER D, SüSSTRUNK S E. Measuring colourfulness in natural images[J]. Electronic imaging, 2003, 5007:87-95.
[9] ZHANG Qiang, HAN Yu, CAI Yunze. Novel full-reference image quality assessment metric based on entropy fusion[J]. Optik-international journal for light and electron optics, 2013, 124(21):5149-5153.
[10] GU Ke, ZHAI Guangtao, YANG Xiaokang, et al. A new reduced-reference image quality assessment using structural degradation model[C]//Proceedings of 2013 IEEE International Symposium on Circuits and Systems. Beijing, China, 2013:1095-1098.
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备注/Memo

备注/Memo:
收稿日期:2018-05-22。
基金项目:中国博士后科学基金项目(2015M572722);安徽省自然科学基金项目(1408085MKL46).
作者简介:王伟,男,1987年生,博士,主要研究方向为计算机视觉、模式识别、智能信息处理。获得国家发明专利授权1项。发表学术论文18篇,被SCI检索3篇,EI检索12篇;刘辉,男,1983年生,博士,主要研究方向为智能信息处理、通信对抗技术。获得国防发明专利授权1项。发表学术论文25篇,被SCI检索4篇,EI检索16篇;杨俊安,男,1965年生,教授,博士生导师,主要研究方向为机器学习、智能信息处理、通信对抗技术。获得国防发明专利授权1项。发表学术论文70余篇,被SCI检索8篇,EI检索30余篇。
通讯作者:王伟.E-mail:wwei009@mail.ustc.edu.cn
更新日期/Last Update: 2018-12-25