基于子带谱间变换的多光谱图像压缩
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摘要
针对星载多光谱图像压缩,提出了基于子带谱间变换的压缩算法。该算法首先对多光谱图像序列的每个波段分别进行空间二维小波变换,以此去除多光谱图像的空间相关性;为了去除多光谱图像的谱间相关性,将小波分解后的每一层子带作为整体,采用串行成对变换的方式对两个波段进行子带谱间KLT变换;最后,利用最优截断的嵌入式块编码算法对变换后的所有主成分同时进行最优率失真压缩。实验结果表明,该算法能够获得较好的压缩性能,同时具有较低的编码复杂度,适用于星载多光谱图像的压缩。
To the problem of spaceborne multi-spectral image compression,a compression scheme based on sub-band spectral transform is proposed. First,two-dimensional discrete wavelet transform is performed to each band of multi-spectral image sequences for removing the spatial correlation. Subsequently,in order to remove the spectral correlation,each level of the sub-bands resulted from wavelet transform are regarded as a whole,and sub-band spectral KLT is performed on the two bands by using serial pairwise transform. Finally,Embedded Block Coding with Optimized Truncation( EBCOT) algorithm is performed on all principal components to realize optimal rate-distortion compression. Experimental results show that the proposed algorithm not only provides better compression performance,but also has lower encoding complexity,which is suitable for the onboard compression of multi-spectral images.
To the problem of spaceborne multi-spectral image compression,a compression scheme based on sub-band spectral transform is proposed. First,two-dimensional discrete wavelet transform is performed to each band of multi-spectral image sequences for removing the spatial correlation. Subsequently,in order to remove the spectral correlation,each level of the sub-bands resulted from wavelet transform are regarded as a whole,and sub-band spectral KLT is performed on the two bands by using serial pairwise transform. Finally,Embedded Block Coding with Optimized Truncation( EBCOT) algorithm is performed on all principal components to realize optimal rate-distortion compression. Experimental results show that the proposed algorithm not only provides better compression performance,but also has lower encoding complexity,which is suitable for the onboard compression of multi-spectral images.
引文
[1]CONOSCENTI M,COPPOLA R,MAGLI E.Constant SNR,rate control,and entropy coding for predictive lossy hyperspectral image compression[J].IEEE Transactions on Image Processing,2016,54(12):7431-7441.
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[6]CARVAJAL G,PENNA B,MAGLI E.Unified lossy and near-lossless hyperspectral image compression based on JPEG2000[J].IEEE Geoscience and Remote Sensing Letters,2008,5(4):593-597.
[7]CAGNAZZO M,POGGI G,VERDOLIVA L.Region-based transform coding of multispectral images[J].IEEE Transactions on Image Processing,2007,16(12):2916-2926.
[8]BITA I P A,BARRET M,PHAM D T.On optimal transforms in lossy compression of multicomponent images with JPEG2000[J].Signal Processing,2010,90(3):759-773.
[9]LEE C,YOUN S,JEONG T,et al.Hybrid compression of hyperspectral images based on PCA with pre-encoding discriminant information[J].IEEE Geoscience and Remote Sensing Letters,2015,12(7):1491-1495.
[10]PENNA B,TILLO T,MAGLI E,et al.Transform coding techniques for lossy hyperspectral data compression[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(5):1408-1421.
[11]BLANES I,SERRA-SAGRISTA J.Pairwise orthogonal transform for spectral image coding[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(3):961-972.
[12]NIAN Y J,LIU Y,YE Z.Pairwise KLT-based compression for multispectral images[J].Sensing and Imaging,2016,17(1):1-15.
[13]徐冬冬,付天骄,张宇.基于改进的K-L变换的多光谱图像压缩算法[J].光电子·激光,2015,26(6):1200-1205.
[14]王平,陈欣,粘永健,等.基于分块KLT的多光谱遥感图像低复杂度有损压缩[J].红外技术,2018,40(2):151-157.
[15]TAUBMAN D.High performance scalable image compression with EBCOT[J].IEEE Transactions on Image Processing,2000,9(7):1158-1170.
[16]吕俊伟,樊利恒,邓江生,等.基于聚类集成的半监督多/高光谱图像分类方法[J].电光与控制,2016,23(5):30-36.
[17]廖佳俊,刘志刚,姜江军.基于改进双边滤波稀疏表示的高光谱目标检测算法[J].电光与控制,2017,24(7):37-40.
[2]TANG X L,CHO S,PEARLMAN W A.Lossy-to-lossless block-based compression of hyperspectral volumetric data[C]//Proceedings of the IEEE International Conference on Image Processing,Atlanta,GA,America,2006:1133-1136.
[3]SHARMA S,DEWAL M L,ANAND R S.Performance evaluation of 3D hybrid transforms and 2D-set partitioning methods for lossy hyperspectral data compression[J].Signal,Image and Video Processing,2015,9(8):1881-1888.
[4]RUCKER J T,FOWLER J E,YOUNAN N H.JPEG2000coding strategies for hyperspectral data[C]//Proceedings of the International Geoscience and Remote Sensing Symposium,Seoul,South Korea,2005:128-131.
[5]PENNA B,TILLO T,MAGLI E,et al.Hyperspectral image compression employing a model of anomalous pixels[J].IEEE Geoscience and Remote Sensing Letters,2007,4(4):664-668.
[6]CARVAJAL G,PENNA B,MAGLI E.Unified lossy and near-lossless hyperspectral image compression based on JPEG2000[J].IEEE Geoscience and Remote Sensing Letters,2008,5(4):593-597.
[7]CAGNAZZO M,POGGI G,VERDOLIVA L.Region-based transform coding of multispectral images[J].IEEE Transactions on Image Processing,2007,16(12):2916-2926.
[8]BITA I P A,BARRET M,PHAM D T.On optimal transforms in lossy compression of multicomponent images with JPEG2000[J].Signal Processing,2010,90(3):759-773.
[9]LEE C,YOUN S,JEONG T,et al.Hybrid compression of hyperspectral images based on PCA with pre-encoding discriminant information[J].IEEE Geoscience and Remote Sensing Letters,2015,12(7):1491-1495.
[10]PENNA B,TILLO T,MAGLI E,et al.Transform coding techniques for lossy hyperspectral data compression[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(5):1408-1421.
[11]BLANES I,SERRA-SAGRISTA J.Pairwise orthogonal transform for spectral image coding[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(3):961-972.
[12]NIAN Y J,LIU Y,YE Z.Pairwise KLT-based compression for multispectral images[J].Sensing and Imaging,2016,17(1):1-15.
[13]徐冬冬,付天骄,张宇.基于改进的K-L变换的多光谱图像压缩算法[J].光电子·激光,2015,26(6):1200-1205.
[14]王平,陈欣,粘永健,等.基于分块KLT的多光谱遥感图像低复杂度有损压缩[J].红外技术,2018,40(2):151-157.
[15]TAUBMAN D.High performance scalable image compression with EBCOT[J].IEEE Transactions on Image Processing,2000,9(7):1158-1170.
[16]吕俊伟,樊利恒,邓江生,等.基于聚类集成的半监督多/高光谱图像分类方法[J].电光与控制,2016,23(5):30-36.
[17]廖佳俊,刘志刚,姜江军.基于改进双边滤波稀疏表示的高光谱目标检测算法[J].电光与控制,2017,24(7):37-40.