Lossless compression of hyperspectral images using spectral and spatial relations 倪銘鴻、張世旭
E-mail: [email protected]
ABSTRACT
Because of the hyperspectral imaging of lossless compression of compression ratio still has room for improvement, so this paper give high compression ratio of lossless compression algorithm.In the literature use of the LUT forecast pixel values of the current bandalgorithm has the advantage of a simple and fast, but due to the hyperspectral image pixel value range is very broad,for example , on the need to carry large amounts of memory. In the LAIS-QLUT algorithm it will look up the desired index value to quantify that can effectively reduce the memory required for checking, and improved compression effect. The hyperspectral imaging with high correlation between the spectrum, in the same band under adjacent pixels also has a high degree of relevance, therefore this Paper using least squares methods and multiband quantization look-up, reduce hyperspectral image superfluous information in spatial and spectral, increase accuracy of pixel the predicted value, and use to predict value selection probability model, Finally use the arithmetic coding and Golomb-rice encoding encodes of the prediction difference. When the Cuprite, Jasper Ridge, Lumar Lake, Moffett Field and Low Altitude after compression tests, Get an the average compression ratio of 3.87. The experimental results prove that this thesis can be effective on hyperspectral image compression.
Keywords : Hyperspectral image、LUT、Least suae、Arithmetic coding、Golomb-rice、AVIRIS Table of Contents
封面內頁 簽名頁 授權書iii 中文摘要iv ABSTRACTv 誌謝vi 目錄vii 圖目錄ix 第一章 緒論1 1.1 前言1 1.2 相關研究2 1.3 論文 架構4 第二章 相關技術探討5 2.1 JPEG-LS5 2.2 查表法6 2.3 LAIS-LUT8 2.4 LAIS-QLUT9 2.5 Golomb rice11 2.6 算術編碼12 2.7 高光譜影像介紹14 第三章 所提出之無失真壓縮編碼17 3.1 系統架構17 3.2 表格初始化19 3.3 預測方法23 3.4 編碼29 3.5 解碼32 第四章 實驗結果34 第五章 結論與未來展望42 5.1 結論42 5.2 未來展望42
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