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首页> 外文期刊>Science in China. Series E, Technological sciences >A hyperspectral image compression algorithm based on wavelet transformation and fractal composition (AWFC)
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A hyperspectral image compression algorithm based on wavelet transformation and fractal composition (AWFC)

机译：基于小波变换和分形合成（AWFC）的高光谱图像压缩算法

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Starting with a fractal-based image-compression algorithm based on wavelet transformation for hyperspectral images, the authors were able to obtain more spectral bands with the help of of hyperspectral remote sensing. Because large amounts of data and limited bandwidth complicate the storage and transmission of data measured by TB-level bits, it is important to compress image data acquired by hyperspectral sensors such as MODIS, PHI, and OMIS; otherwise, conventional lossless compression algorithms cannot reach adequate compression ratios. Other loss-compression methods can reach high compression ratios but lack good image fidelity, especially for hyperspectral image data. Among the third generation of image compression algorithms, fractal image compression based on wavelet transformation is superior to traditional compression methods, because it has high compression ratios and good image fidelity, and requires less computing time. To keep the spectral dimension invariable, the authors compared the results of two compression algorithms based on the storage-file structures of BSQ and of BIP, and improved the HV and Quadtree partitioning and domain-range matching algorithms in order to accelerate their encode/decode efficiency. The authors' Hyperspectral Image Process and Analysis System (HIPAS) software used a VC++6.0 integrated development environment (IDE), with which good experimental results were obtained. Possible modifications of the algorithm and limitations of the method are also discussed.
机译：从基于小波变换的基于分形的高光谱图像压缩算法开始，作者借助高光谱遥感技术能够获得更多的光谱带。由于大量数据和有限的带宽使通过TB级比特测量的数据的存储和传输变得复杂，因此压缩由MODIS，PHI和OMIS等高光谱传感器获取的图像数据非常重要；否则，传统的无损压缩算法将无法达到足够的压缩率。其他损耗压缩方法可以达到较高的压缩率，但缺乏良好的图像保真度，尤其是对于高光谱图像数据而言。在第三代图像压缩算法中，基于小波变换的分形图像压缩具有较高的压缩率和良好的图像保真度，并且需要较少的计算时间，因此优于传统的压缩方法。为了保持频谱尺寸不变，作者比较了两种基于BSQ和BIP的存储文件结构的压缩算法的结果，并改进了HV和Quadtree分区以及域范围匹配算法，以加速其编码/解码效率。作者的高光谱图像处理和分析系统（HIPAS）软件使用了VC ++ 6.0集成开发环境（IDE），并获得了良好的实验结果。还讨论了算法的可能修改形式和方法的局限性。

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