基于上下文窗口中反向搜索的高光谱图像无损压缩
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摘要
针对基于单波段预测的高光谱图像无损压缩压缩比低的问题,提出基于上下文窗口中反向搜索的高光谱图像无损压缩算法。首先,对待测像素设定上下文窗口,计算其预测参考值并进行反向搜索预测得到待测像素的候选预测值。然后,选取与预测参考值最接近的候选预测值作为待测像素的最终预测结果。最后,对预测残差图像进行一阶算术编码完成压缩过程。利用提出的算法对AVIRIS 1997高光谱图像进行了实验,结果显示,提出的算法通过对上下文窗口、等效系数和有效像素阈值的优化取值,使反向搜索预测的效果达到最好,经过算术编码器编码后,可以得到一个3.63倍的平均压缩比。该方法具有较低的算法复杂度和内存需求,优于当前已报道的基于单波段预测的其他各种高光谱图像无损压缩算法。
An efficient lossless compression scheme of hyperspectral images based on one-band-prediction was proposed by using backward search in a context window to improve the compression ratio of the hyperspectral images.Firstly,the context window for a pixel to be tested was defined and the prediction reference value under testing was calculated.Then,the candidate predictors which were mostly closed to the prediction reference value were selected as the final prediction results of the pixel to be measured.Finally,the predicted residual image was coded by a first-order arithmetic to implement the image compression.The method proposed was used in the experiments on hyperspectral images from Airborne Visible/Infrared Imaging Spectrometer(AVIRIS)1997,and the results show that the method has obtained the best prediction results of backward search by optimizing the context window,equivalent coefficients and effective pixel thresholds.After coding by an arithmetic coder,the average compression ratio is 3.63。The method has a lower computing complexity and smaller memory requirements,and outperforms all other lossless compression schemes for hyperspectral images thathave been previously reported.
An efficient lossless compression scheme of hyperspectral images based on one-band-prediction was proposed by using backward search in a context window to improve the compression ratio of the hyperspectral images.Firstly,the context window for a pixel to be tested was defined and the prediction reference value under testing was calculated.Then,the candidate predictors which were mostly closed to the prediction reference value were selected as the final prediction results of the pixel to be measured.Finally,the predicted residual image was coded by a first-order arithmetic to implement the image compression.The method proposed was used in the experiments on hyperspectral images from Airborne Visible/Infrared Imaging Spectrometer(AVIRIS)1997,and the results show that the method has obtained the best prediction results of backward search by optimizing the context window,equivalent coefficients and effective pixel thresholds.After coding by an arithmetic coder,the average compression ratio is 3.63。The method has a lower computing complexity and smaller memory requirements,and outperforms all other lossless compression schemes for hyperspectral images thathave been previously reported.
引文
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[11]LIN CH CH,HWANG Y T.An efficient lossless compression scheme for hyperspectral images using two-stage prediction[J].IEEE Geoscience Remote Sensing Letters,2010,7(3):558-562.
[12]SONG J W,ZHANG ZH W,CHEN X M.Lossless compression of hyperspectral imagery via RLS filter[J].Electronics Letters,2013,49(16):992-993.
[13]MIELIKAINEN J,TOIVANEN P.Clustered DPCM for the lossless compression of hyperspectral images[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(12):2943-2946.
[14]MIELIKAINEN J,HUANG B.Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length[J].IEEE Geoscience and Remote Sensing Letters,2012,9(6):1118-1121.
[15]WANG K Y,LIAO H L,LI Y S,et al..Lossless compression of hyperspectral images using CDPCM-APL with reference bands selection[J].Proceedings of SPIE,2014,9124:91240 W-1-91240 W-10.
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[17]ABERG J,SHTARKOV Y M,SMETTS B J M.Multialphabet coding with separate alphabet description[C].Proceedings of Compression and Complexity of Sequences,1997,56-65.
[18]ORLITSKY A,SANTHANAM N P,ZHANG J.Universal compression of memoryless sources over unknown alphabets[J].IEEE Transactions on Information Theory,2004,50(7):1469-1481.
[19]RIZZO F,CARPENTIERI B,MOTTA G,et al..Low-complexity lossless compression of hyperspectral imagery via linear prediction[J].IEEE Signal Processing Letters,2005,12(2):138-141.
[20]KLIMESH M.Low-complexity adaptive lossless compression of hyperspectral imagery[J].Proceedings of SPIE,2006,6300:63000N-1-63000N-9.
[21]MAGLI E.Multiband lossless compression of hyperspectral images[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(4):1168-1178.
[22]AIAZZI B,AIPARONE L,BARONTI S,et al..Crisp and fuzzy adaptive spectral predictions for lossless and near-lossless compression of hyperspectral imagery[J].IEEE Geoscience and Remote Sensing Letters,2007,4(4):532-536.
[2]MIELIKAINEN J.Lossless compression of hyperspectral images using lookup tables[J].IEEE Signal Processing Letters,2006,13(3):157-160.
[3]HUANG B,SRIRAJA Y.Lossless compression of hyperspectral imagery via lookup tables with predictor selection[J].Proceedings of SPIE,2006,6365:65650L-1-63650L-8.
[4]MIELIKAINEN J,TOIVANEN P.Lossless compression of hyperspectral images using aquantized index to lookup tables[J].IEEE Geoscience and Remote Sensing Letters,2008,5(3):474-478.
[5]宋金伟,张忠伟,陈晓敏.利用线性预测与查表法的高光谱图像压缩[J].光学精密工程,2013,21(8):2201-2208.SONG J W,ZHANG ZH W,CHEN X M.Hyperspectral imagery compression via linear prediction and lookup tables[J].Opt.Precision Eng.,2013,21(8):2201-2208.(in Chinese)
[6]汤毅,辛勤,李纲,等.基于内容的高光谱图像无损压缩[J].光学精密工程,2012,20(3):668-674.TANG Y,XIN Q,LI G,et al..Lossless compression of hyperspectral images based on contents[J].Opt.Precision Eng.,2012,20(3):668-674.(in Chinese)
[7]粘永健,辛勤,汤毅,等.基于多波段预测的高光谱图像分布式无损压缩[J].光学精密工程,2012,20(4):906-912.NIAN Y J,XIN Q,TANG Y,et al..Distributed lossless compression of hyperspectral images based on multi-band prediction[J].Opt.Precision Eng.,2012,20(4):906-912.(in Chinese)
[8]尹传历,李嘉全.基于位平面的嵌入式超光谱图像压缩系统[J].液晶与显示,2012,27(2):245-249.YIN CH L,LI J Q.Embedded hyper spectral image compression system based on bit-plane[J].Chinese Journal of Liquid Crystals and Displays,2012,27(2):245-249.(in Chinese)
[9]殷亚男,王晓东,李丙玉.基于预测和JPEG2000的MODIS红外辐射多光谱图像无损压缩算法[J].液晶与显示,2013,28(6):922-926.YIN Y N,WANG X D,LI B Y.Lossless compression method based on prediction and JPEG2000for MODIS emissive IR bands multispectral image[J].Chinese Journal of Liquid Crystals and Displays,2013,28(6):922-926.(in Chinese)
[10]KIELY A,KLIMESH M.Exploiting calibrationinduced artifacts in lossless compression of hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(8):2672-2678.
[11]LIN CH CH,HWANG Y T.An efficient lossless compression scheme for hyperspectral images using two-stage prediction[J].IEEE Geoscience Remote Sensing Letters,2010,7(3):558-562.
[12]SONG J W,ZHANG ZH W,CHEN X M.Lossless compression of hyperspectral imagery via RLS filter[J].Electronics Letters,2013,49(16):992-993.
[13]MIELIKAINEN J,TOIVANEN P.Clustered DPCM for the lossless compression of hyperspectral images[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(12):2943-2946.
[14]MIELIKAINEN J,HUANG B.Lossless compression of hyperspectral images using clustered linear prediction with adaptive prediction length[J].IEEE Geoscience and Remote Sensing Letters,2012,9(6):1118-1121.
[15]WANG K Y,LIAO H L,LI Y S,et al..Lossless compression of hyperspectral images using CDPCM-APL with reference bands selection[J].Proceedings of SPIE,2014,9124:91240 W-1-91240 W-10.
[16]MOFFAT A,NEAL R,WITTEN I H.Arithmetic coding revisited[C].Data Compression Conference,1995,202-211.
[17]ABERG J,SHTARKOV Y M,SMETTS B J M.Multialphabet coding with separate alphabet description[C].Proceedings of Compression and Complexity of Sequences,1997,56-65.
[18]ORLITSKY A,SANTHANAM N P,ZHANG J.Universal compression of memoryless sources over unknown alphabets[J].IEEE Transactions on Information Theory,2004,50(7):1469-1481.
[19]RIZZO F,CARPENTIERI B,MOTTA G,et al..Low-complexity lossless compression of hyperspectral imagery via linear prediction[J].IEEE Signal Processing Letters,2005,12(2):138-141.
[20]KLIMESH M.Low-complexity adaptive lossless compression of hyperspectral imagery[J].Proceedings of SPIE,2006,6300:63000N-1-63000N-9.
[21]MAGLI E.Multiband lossless compression of hyperspectral images[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(4):1168-1178.
[22]AIAZZI B,AIPARONE L,BARONTI S,et al..Crisp and fuzzy adaptive spectral predictions for lossless and near-lossless compression of hyperspectral imagery[J].IEEE Geoscience and Remote Sensing Letters,2007,4(4):532-536.