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標題: 應用於醫學與高光譜衛星影像之無失真壓縮技術與嵌入式系統實現
Lossless compression techniques for medical and hyperspectral images and their embedded system implementation
作者: 洪瑞廷
Hung, Ruei-Ting
關鍵字: lossless compression system;高光譜衛星影像;embedded SW/HW co-design;JPEG-LS;LUT-Based;Wiener filtering;SW/HW co-design;AVIRIS;ARM926-EJS CPU;Programmed I/O;DMA;VLSI;嵌入式平台;JPEG-LS;LUT-Based;Wiener filtering;SW/HW co-design;AVIRIS;ARM926-EJS CPU;Programmed I/O;DMA;人造衛星
出版社: 電機工程學系所
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另外,所提出之多頻帶壓縮系統能應用於高光譜衛星影像上,為基於LUT-Based [41-44]的作法所提出之改良演算法,其中提出二個新穎的方式來提昇效能。第一個方式,為利用空間域上的相關性,預測方式可表示成如Wiener filtering處理形式,並在頻帶域上,由一階的預測器延展至多階的預測器。第二個方式,為提出反向搜尋方式並搭配量化索引技術,能達到如同使用查表的作法,並大量的降低記憶體與搜尋時間。而在實現上,演算法也能依據記憶體使用量、運算速度與編碼效能搭配不同的版本作為實現,並且可提供參數化的設定與編碼上的調整,而實驗於NASA所提供之5個AVIRIS測試影像上,所提出之最好版本能壓縮至近4倍之壓縮倍率,優於最新的技術。
多頻帶壓縮系統為實現於ARM926-EJS CPU與Xilinx FPGA之嵌入式平台。而基於軟硬體共設計之概念,首先,為分析演算法中各模組之運算複雜度,並抽出運算時間最長的Wiener filtering模組作硬體加速IP,能掛載於AHB匯流排上。而IP的設計上,使用到許多VLSI設計之技巧,如:管線化、平行化、資料共用等;在韌體設計上,也可採用Programmed I/O與DMA之方式作為軟硬體溝通之媒介,其中以DMA之方式能提昇搬移的效能,而硬體模組之執行時間比起純軟體之運算,提昇了85倍;在軟體端,也加強程式設計的風格,並搭配高階的編譯器以優化程式碼與執行效能。整體系統執行時間,約提昇了10倍,而以此嵌入式系統之壓縮器能符合搭載於人造衛星或飛行器上作為即時壓縮與傳輸之需求。

In this thesis, an effective lossless compression scheme for signle band and multi-band images are presented. Besides, a demonstration of lossless compression system based on embedded SW/HW co-design is also implemented. Fisrtly, the proposed signle band compression algorithm is suitable for medical image compression and employs a linear adaptive predictor and binary mode to perform robustly on any type of image texture. To suppress the prediction residules and improve the performance of conditional entropy coding, an error feedback mechanism is employed and different directions of gradient are incorporated to bulid entropy model. The simulation results indicate the proposed scheme has better compression ratio than the well known JPEG-LS standard.
Secondly, the proposed multi-band image compression is applied to hyperspectral images. It adopts a table look-up approach in prediction and employs two novel measures to improve the compression performance. The first measure takes advantage of the spatial data correlation and formulates the derivation of a spectral domain predictor as a process of Wiener filtering. Under the Wiener filtering framework, the proposed predictor can be extended from one-tap to multi-tap prediction to further improve performance. In the second measure, a backward search scheme and the quantization index approach are used instead of look-up tables which reduces the memory storage requirement drastically and search effort achieve performance equivalent to that obtained using multiple look-up tables. Variations of the proposed algorithms can be derived subject to the requirement of memory bandwidth, computation complixity and compression efficiency. Simulations on parameter settings and refinements on entropy coding are conducted to fine-tune performance. Experiments on 5 sequences of AVIRIS images show that the proposed algorithm can best yield an average compression ratio of as high as nearly 4 better than other state-of-art algorithms.
Finally, following the principle of HW/SW co-design, the compression algorithm is implemented on an embedded platform composed of ARM 926-EJS CPU and Xilinx FPGA. We first profile the execution time of each module in the algorithm, and choose Wiener filtering process for hardware realization. Various the VLSI design techniques, e.g. pipelined, parallel processing and data reused are explored, which speeds up the excution of Wiener filtering by a factor of 85 than the software design. Programmed I/O and DMA drivers are supported as a medium in HW/SW communication. In the software part, coding style improvement and high level compiler option are sought to optimize the code size and execution time. The implementation results show a 10 times speed up compared to a purely software compressor.
其他識別: U0005-1708200919442400
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