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小波变换在气象卫星云图压缩中的应用
The Application of Wavelet Transform to Satellite Cloud Image Compression
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摘要: 该文分析了卫星云图和自然图像在小波系数分布上的特征差异，提出了基于小波变换的气象卫星云图压缩方案。方案选择对卫星云图压缩效果较好的、具有双正交性的Odegard 9/7滤波器组对卫星云图进行五级小波分解和重构; 根据卫星云图小波分解系数相似性强、低频分量能量更为集中、分量系数层次衰减性明显的特点，使用改进后的零树编码算法对小波系数进行编码运算; 最后，采用高效的自适应算术编码对输出数据流进行了进一步的压缩。该方法对卫星云图的压缩效果要优于经典的嵌入式零树小波编码，在失真允许情况下，对红外云图的最大压缩比可达40:1，水汽云图达60:1, 可见光云图达35:1。Abstract: Different characteristics between meteorological satellite cloud images and common images are analyzed, and a compression method of meteorological satellite cloud images is proposed based on wavelet transform (WT). First, the bio-rthogonal Odegard 9/7 filter is selected, which shows better compression ability for cloud images. Using this filter, a cloud image is five step decomposed and reorganized. It's found that the wavelet coefficients of cloud image have more similarity and obvious attenuation in different steps, the wavelet coefficients are encoded using the improved 0 tree quantization algorithm. Finally, a further compression is processed for the output data streams using the adaptive arithmetic coder. The method proposed performs better in cloud image compression than other existing compression methods, e.g., the classic embedded 0 tree wavelet (EZW) introduced by Shapiro. And within the anamorphic error range, the highest ratio of cloud image compression is 40:1 for infrared image, 60:1 for water vapor image and 35:1 for visible image.
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Key words:
- satellite cloud image;
- image compression;
- wavelet;
- 0 tree quantization
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图 2 五级小波分解的低频分量系数分布图
Fig. 2 Low frequency component coefficient of five-step wavelet decomposed
图 3 五级小波分解的水平分量系数分布
Fig. 3 Horizontal component coefficient of five-step wavelet decomposed
图 4 基于小波变换的卫星云图压缩方案结构框图
Fig. 4 Flow chart of meteorological satellite cloud image compression method based on wavelet transform
表 1 各滤波器组恢复图像质量比较
Table 1 Comparison of the recovering image quality using different filters
表 2 Antoninni和Odegard恢复云图质量比较
Table 2 Comparison of Antoninni and Odegard recovering image
表 3 EZW算法和本文算法压缩效果（峰值信噪比）比较
Table 3 Comparison between EZWmethod and the method in this paper
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