[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(6):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(6):989-993.[doi:10.11992/tis.201805027]
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WANG Wei,LIU Hui,YANG Junan.Blind quality evaluation with image features codebook mapping[J].CAAI Transactions on Intelligent Systems,2018,13(6):989-993.[doi:10.11992/tis.201805027]
一种特征字典映射的图像盲评价方法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第6期
页码:
989-993
栏目:
学术论文—机器感知与模式识别
出版日期:
2018-10-25
- Title:
- Blind quality evaluation with image features codebook mapping
- Keywords:
- objective assessment; blind assessment; image quality assessment; local structural feature; global statistics characteristics; feature extraction; codebook; pooling mapping
- 分类号:
- TP391.4
- 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.
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[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.
[5] LI Chaofeng, BOVIK A C. Content-partitioned structural similarity index for image quality assessment[J]. Signal processing:image communication, 2010, 25(7):517-526.
[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.
[11] ROTH S, BLACK M J. Fields of experts:a framework for learning image priors[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005:860-867.
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