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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94546


    Title: 應用於H.264AVC視訊壓縮的整數離散餘弦轉換之退化型壓縮感測演算法研究
    Other Titles: Degradation algorithm of compressive sensing for integer DCT transform with application to H.264/AVC video compression
    Authors: 吳哲維;Wu, Che-Wei
    Contributors: 淡江大學電機工程學系碩士班
    陳巽璋;Chern, Shiunn-Jang
    Keywords: 低複雜度;壓縮感測;整數離散餘弦轉換;退化型壓縮感測;信號重建;Low complexity;Compressive sensing;Integer DCT;Degradation CS;Signal reconstruction
    Date: 2013
    Issue Date: 2014-01-23 14:45:26 (UTC+8)
    Abstract: 傳統影像資料例如由照相機取得,在類比轉數位(Analog-to-digital; A/D)的取樣過程(Sampling process)中,其取樣率依據取樣定理(Sampling theory)至少須為訊號頻寬的二倍,所取樣出的離散取樣資料其量非常可觀,然後在傳送過程也須經由資料壓縮並透過多媒體網路來傳送這種取樣與壓縮方式其過程的確浪費了大量的取樣資源。
    本篇論文探討一種新的信號處理技術,稱之為壓縮感測(Compressive sensing; CS),被提出並廣泛應用在視訊與通訊訊號處理領域中,有別於傳統作法,壓縮感測技術的特色在於其取樣技術是針對具稀疏性(Sparsity)或可壓縮(Compressive)的訊號源,即在取樣時直接針對訊號進行壓縮的新興理論,此種作法容許我們所取樣的原始信號頻寬可以低於傳統取樣定理的要求。
    基本上傳統壓縮感測(CS)理論是基於假設稀疏值訊號向量(Signal vector)的位置是未知的,這樣會使得在許多實際應用上有許多限制,但是在許多情況中,稀疏值的位置於接收端是可以預知的,因此所謂退化壓縮感測演算法被提出,退化型的演算法[11](Degradation algorithm of CS)被設計用於信號獲取(Acquisition),並利用被檢測出的大多數稀疏值,經由線性處理來進行訊號之重建(Reconstruction)。相較於傳統其他類似的壓縮感測方法,退化型壓縮感測演算法可以有效減少感測數量及改善操作效率。
    最後,我們可以由電腦模擬的結果,驗證我們所提出的方法。
    In the conventional image/video compression approach, we need to first capture the image/video signals from for example camera, and take more sampled data via sampling processes. For transmission those sampled data through various communication networks, high efficient compression algorithm is required for compressing data [2-8]. This processes of sampling analog signal and then compressing them for reducing the quantity of sampled data is a kind of wasting. Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. It has been developed from questions raised about the efficiency of the conventional signal processing pipeline for compression, coding and recovery of natural signals, including audio, still images and video. With the basic principle developed in CS, we might enable dramatically reduced measurement time, reduced sampling rates significantly, or reduced use of Analog-to-Digital converter resources.
    Many natural signals have concise representations when expressed in the proper basis. Recently, for data acquisition and signal recovery based on the premise that a signal having a sparse representation in the proper basis, the technique of degradation algorithm of CS [11] was presented for image compression. It showed that the complexity as well as signal reconstruction quality could be improved significantly.
    Via computer simulation, we verify that the performance is improved, in terms of the PSNR and the efficiency of the system.
    Appears in Collections:[電機工程學系暨研究所] 學位論文

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