| 提升小波变换与分形结合的图像压缩算法 |
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| 引用本文： | 马俐,赵红东,Hafiz Shehzad Ahme,彭晓灿,韩铁成. 提升小波变换与分形结合的图像压缩算法[J]. 电视技术, 2017, 41(2): 11-15. DOI: 10.16280/j.videoe.2017.02.003 |
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| 作者姓名： | 马俐 赵红东 Hafiz Shehzad Ahme 彭晓灿 韩铁成 |
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| 作者单位： | 河北工业大学电子信息工程学院,天津,300401 |
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| 基金项目： | 河北省自然基金（编号F2013202256） |
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| 摘 要： | 遥感图像在环境监测、军事侦察等多方面有着广泛应用,然而遥感图像包含信息量大,对其进行压缩来提高存储效率具有重要意义.传统分形编码由于压缩比大的特点被广泛应用到遥感图像压缩中,但是传统分形编码存在压缩时间太长的问题.提出提升小波变换与改进分形结合的压缩方法,把提升小波变换后的低频分量进行基于最小方差搜索法的分形压缩.实验结果表明,提升小波变换与改进的分形结合的压缩方法与小波变换与分形结合的压缩方法相比,在峰值信噪比保持在35 dB不变的情况下,压缩时间大约可以缩短8倍,图像压缩比也有提高.
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| 关 键 词： | 提升小波变换 遥感图像 分形编码 |
| 收稿时间： | 2016-05-20 |
| 修稿时间： | 2016-06-29 |
Image compression algorithm based on the combination of lifting wavelet transform and fractal |
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| Li M,Hongdong Zhao,Hafiz Shehzad Ahme,Xiaocan Peng and Tiecheng Han. Image compression algorithm based on the combination of lifting wavelet transform and fractal[J]. Ideo Engineering, 2017, 41(2): 11-15. DOI: 10.16280/j.videoe.2017.02.003 |
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| Authors: | Li M Hongdong Zhao Hafiz Shehzad Ahme Xiaocan Peng Tiecheng Han |
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| Affiliation: | Electronic and Information Engineering,Hebei University of Technology,Electronic and Information Engineering,Hebei University of Technology,Electronic and Information Engineering,Hebei University of Technology,Electronic and Information Engineering,Hebei University of Technology,Electronic and Information Engineering,Hebei University of Technology |
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| Abstract: | Remote Sensing Image is widely applied in environmental monitoring, military reconnaissance and other aspects. However, remote sensing images contain a large amount of information, it is important to improve storage efficiency. Traditional fractal coding has been widely applied to the remote sensing image compression due to the high compression ratio. But the compression time of the traditional fractal coding is too long. A compression method based on the combination of lifting wavelet transform and the fractal is proposed in this paper, making the best of the coefficients distribution, the algorithm is consisted of improved fractal coding in low-frequency of lifting wavelet transform coefficients. Experimental results show that the compression time can be shorted about 8 times, and the compression ratio is improved compared with the former unimproved method based on the Peak signal to noise ratio(PSNR) is maintained at 35dB unchanged. |
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| Keywords: | lifting wavelet transform remote sensing image fractal coding |
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