摘要
干涉光谱图像具有自身的特点,相邻谱线之间的相关性较弱,谱线数据也有自身的特征,主干涉区域数据变化剧烈,而其它区域的数据呈现单调变化的趋势。根据这些特点,该文提出一种数据区域分类方法对光谱数据进行分类处理,将一根谱线的数据分为主干涉区域与非主干涉区域两类,主干涉区域采用数据相似匹配进行描述,而对非主干涉区域采用二次曲线拟合方法进行数据分析,这种数据分析方法有利于提高该类图像编码效率。仿真结果表明,该方法可以降低无损压缩输出码率达0.2-0.4bpp,并且可以提高有损压缩压缩效率。
Interference spectral images have their own features. The correlations between the spectral lines are weak. And the data in a spectral line have particularity, that is, the data vary abruptly in the main district and the rest data change monotonously. On the basis of analyzing the particularity, a novel method for data classification is proposed. The data of a spectral line are decomposed to two classes, called main-interference class and no main-interference class. And a similarity-based match method is presented for the data of main-interference class, while the data of no main-interference class is processed by another method called 2-order curve-fitting algorithm. The data of a spectral line can be approached appropriately in the ways discussed above, which avails for image compression. The experimental results show that the output rate decreases by 0.2-0.4bpp for lossless compression, and also can improve the loss compression efficiency.
出处
《电子与信息学报》
EI
CSCD
北大核心
2007年第5期1140-1144,共5页
Journal of Electronics & Information Technology
基金
国家自然科学基金重点项目(60532060)
海南省教育厅科研基金(Hjkj200602)
海南省自然科学基金(80551)资助课题
关键词
图像处理
图像压缩
二次曲线拟合
相似匹配
无损压缩
Image processing
Image compression
2-order curve-fitting
Similarity-based match
Lossless compression
