[1]李洪均,谢正光,王伟.小波域的灰色关联度图像压缩[J].东南大学学报(自然科学版),2017,47(2):236-241.[doi:10.3969/j.issn.1001-0505.2017.02.007]
 Li Hongjun,Xie Zhengguang,Wang Wei.Image compression based on grey relation in wavelet domain[J].Journal of Southeast University (Natural Science Edition),2017,47(2):236-241.[doi:10.3969/j.issn.1001-0505.2017.02.007]
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小波域的灰色关联度图像压缩()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第2期
页码:
236-241
栏目:
电子科学与工程
出版日期:
2017-03-20

文章信息/Info

Title:
Image compression based on grey relation in wavelet domain
作者:
李洪均谢正光王伟
南通大学电子信息学院, 南通 226019
Author(s):
Li Hongjun Xie Zhengguang Wang Wei
School of Electronic Information Engineering, Nantong University, Nantong 226019, China
关键词:
图像压缩 图像稀疏表示 灰色关联度 小波变换
Keywords:
image compression image sparse representation grey relation wavelet transform
分类号:
TN216
DOI:
10.3969/j.issn.1001-0505.2017.02.007
摘要:
为了改善小波变换的图像稀疏表示性能,提出了一种小波域的灰色关联度图像压缩算法.首先,利用小波变换对测试图像进行分解,获得不同区域的小波系数;然后,利用小波系数特点,将灰色关联度用于系数关联度的刻画中,并计算不同尺度间系数的灰色关联度;根据小波系数区域特征,将小波系数进行分类,构造出不同系数类型下的稀疏表示方法;最后,将该算法应用于图像压缩.实验结果表明,在相同压缩率下,所提算法的客观评价指标峰值信噪比较现有同类算法提高了1.04~3.65 dB,图像主观视觉质量明显提高.所提算法能够结合系数特征和视觉特性自适应地构造字典,提高了图像稀疏表示能力,进一步提高了图像压缩性能.
Abstract:
To improve the ability of image sparse representation of wavelet transform, an image compression algorithm based on the grey relation in the wavelet domain is proposed. First, the test image is decomposed by wavelet transform, and the wavelet coefficient in each scale is obtained. Then, based on the characteristics of the wavelet coefficients, the relation of the coefficients is described based on the grey relation, and the grey relation of the coefficients among different scales is calculated. The wavelet coefficients are classified according to the regional characteristics and the sparse representation method with different types of coefficients is developed. Finally, the proposed algorithm is applied to image compression. The experimental results show that, with the same compression ratio, the objective evaluation index, the ratio of the peak signal to the noise ratio(PSNR), of the compressed image increases by 1.04 to 3.65 dB compared with that of the existing similar algorithms, and the subjective visual quality of the image is also improved. The proposed algorithm can adaptively construct a dictionary by combining the characteristics of coefficients and visual properties, thus improving the ability of image sparse representation and enhancing the image compression performance.

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备注/Memo

备注/Memo:
收稿日期: 2016-08-12.
作者简介: 李洪均(1981—),男,博士,副教授,lihongjun@ntu.edu.cn.
基金项目: 国家自然科学基金资助项目(61371111,61371112,61371113,61401241,61601248)、江苏省高校自然科学研究面上资助项目(16KJB510036)、江苏高校品牌专业建设工程资助项目(PPZY2015B135)、江苏省基础研究计划(自然科学基金)资助项目(BK20130393)、南通市应用研究计划基金资助项目(MS12016025).
引用本文: 李洪均,谢正光,王伟.小波域的灰色关联度图像压缩[J].东南大学学报(自然科学版),2017,47(2):236-241. DOI:10.3969/j.issn.1001-0505.2017.02.007.
更新日期/Last Update: 2017-03-20