Lossless compression of hyperspectral images based on 2D/3D hybrid adaptive prediction - 文章详情页

作者：

Chai, Yan (Chai, Yan.) | Zhang, Xiao-Ling (Zhang, Xiao-Ling.) | Shen, Lan-Sun (Shen, Lan-Sun.)

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摘要：

Using　the　significant　spectral　correlation　within　the　hyperspectral　images,　we　present　a　lossless　compression　algorithm　in　this　paper.　The　method　includes　four　key　steps:　(1)　band　ordering　according　to　spectral　correlation　coefficient;　(2)　2-D　and　3-D　hybrid　adaptive　prediction　based　on　local　texture　and　neural　networks,　respectively;　3)　hybrid　prediction　based　on　the　correlation　of　neighborhood;　4)　context-based　Golomb　coding.　Experimental　results　show　that　this　method　can　remove　the　spatial　and　spectral　redundancy　efficiently　and　outperforms　JPEG-LS　and　3D-APA　on　average　bit　rate　obviously.

关键词：

Algorithms Forecasting Image coding Image compression Neural networks Redundancy Remote sensing Textures Three dimensional Two dimensional

作者机构：

[ 1 ] [Chai, Yan]Signal and Information Processing Laboratory, Beijing University of Technology, Beijing 100022, China
[ 2 ] [Zhang, Xiao-Ling]Signal and Information Processing Laboratory, Beijing University of Technology, Beijing 100022, China
[ 3 ] [Shen, Lan-Sun]Signal and Information Processing Laboratory, Beijing University of Technology, Beijing 100022, China

通讯作者信息：

电子邮件地址：

chaiyan@emails.bjut.edu.cn

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