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适用于楔形滤光片型光谱成像仪的快速无损数据压缩方法
引用本文:李洪波,胡炳樑,余璐,魏儒义,于涛.适用于楔形滤光片型光谱成像仪的快速无损数据压缩方法[J].光谱学与光谱分析,2019,39(1):297-302.
作者姓名:李洪波  胡炳樑  余璐  魏儒义  于涛
作者单位:中国科学院西安光学精密机械研究所,陕西 西安 710119;中国科学院大学,北京 100049;中国科学院西安光学精密机械研究所,陕西 西安,710119
基金项目:国家重大科研仪器项目(11727806),国家自然科学基金面上项目(11573058),国家重点研发计划项目(2017YFC1403700,2016YFC0201102)资助
摘    要:楔形滤光片型光谱成像仪具有无运动部件、低光机复杂度等优点,是低成本微型化光谱成像仪的一个重要发展方向。不同于传统色散型光谱成像仪,楔形滤光片型光谱成像仪获取的数据是光谱-空间混合调制的图像。针对直接应用CCSDS123进行楔形滤光片型光谱成像仪数据压缩时压缩比较低的问题,结合楔形滤光片型光谱成像仪"谱像混合"、"推扫成谱"的特点,通过定义新的局部差向量,构建了一种低运算复杂度适合硬件实现的快速无损压缩方法 WCCSDS123。新的局部差向量中参与计算的像元集合代表的是同一被观测点的光谱信息。WCCSDS123方法首先利用局部和与改进的局部差向量对采样点的值进行预测,再利用预测值与真实值计算预测残差并对其进行整数映射,最后采用采样自适应熵编码对映射预测残差进行编码完成压缩。在6组楔形滤光片型光谱成像仪数据上分别采用WCCSDS123和CCSDS123进行了压缩实验。实验结果表明,与CCSDS123相比,WCCSDS123的压缩比提高了约21.62%,压缩耗时没有明显差异。因此,该方法在提高压缩比同时,继承了CCSDS123复杂度低,易于硬件实现的优点。该方法 WCCSDS123具有较低的计算复杂度,能够更加有效地利用空间光谱冗余信息,获得更好的压缩效果,是针对楔形滤光片型光谱成像仪的一种良好的快速无损数据压缩方法。

关 键 词:楔形滤光片  光谱成像仪  无损压缩  CCSDS123  局部差向量
收稿时间:2017-12-13

A Fast Lossless Data Compression Method for the Wedge Filter Spectral Imager
LI Hong-bo,HU Bing-liang,YU Lu,WEI Rui-yi,YU Tao.A Fast Lossless Data Compression Method for the Wedge Filter Spectral Imager[J].Spectroscopy and Spectral Analysis,2019,39(1):297-302.
Authors:LI Hong-bo  HU Bing-liang  YU Lu  WEI Rui-yi  YU Tao
Institution:1. Laboratory of Spectral Imaging Technique, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China 2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:Wedge filter spectral imager, with no moving components and low complexity, has become an important development direction of low cost miniature imaging spectrometer. Based on the state of the art hyperspectral lossless compression standard CCSDS123, we propose a lossless data compression method for the wedge filter spectral imager. The proposed method redefines the local difference vector in CCSDS123, taking fully advantage of the spatial-spectral co-modulation characteristics of the wedge filter spectral imager. To compress the raw data from a wedge filter spectral imager, the compression encoder firstly predicts the sample value using its local sum and local difference vector, then computes a prediction residual and the corresponding mapped prediction residual, finally encodes the mapped prediction residual via a sample-adaptive entropy coding approach. The proposed method can effectively compress the raw data from a wedge filter spectral imager by using the local correlation in the spatial-spectral space. To verify the compression performance of the proposed method, experiments are taken on 6 raw datasets containing different scenes. The results show that the proposed method surpasses the original CCSDS123 method by about 21.62% higher compression ratio on the test datasets with almost the same computational time.
Keywords:Wedge filter  Spectral imager  Lossless compression  CCSDS123  Local difference vector  
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