臺灣博碩士論文加值系統

近年來多數位媒體通信技術進步，加上網際網路的蓬勃發展，數位科技的應用越來越廣泛。其中，數位電視地面廣播(Digital Video Broadcasting-Terrestrial, DVB-T)採用MPEG-2的編碼標準，是以數位形式傳輸整合視訊與音訊的通訊服務，是使用者最多的一個國際標準。由於傳送影像畫面需配合交流電的脈衝週期，每1/60秒觸發一個畫面，因此採用交錯式畫面傳輸。解交錯掃瞄器是數位電視控制器內重要的核心技術之一，主要是將電視訊號所用的交錯式掃瞄轉換為目前各種新型顯示裝置如LCD TV、PDP TV等所用的循序掃瞄。由於這個轉換的動作需要高速存取之緩衝器儲存多張影像畫面的資料，以進行解交錯程序。因此用到相當大的記憶體，使得LCD TV的成本提高。本研究中，我們結合霍夫曼編碼與STELA解交錯的演算法，用來降低記憶體的使用需求，並且能夠完成解交錯的動作，如此可以使得LCD TV的成本降低。本論文最主要的貢獻是當記憶體的需求超過所預設的額定大小時，可以機動的調整壓縮策略，將無法壓縮儲存的掃瞄線之錯誤有效的分散到整個畫面中，提供視覺上可接受的畫質。

The multimedia communication technology has been largely improved in recent years. The prevalence of Internet also helps to promote the digital technology applications. The most successful international multimedia standard for consumer electronic is Digital Video Broadcasting-Terrestrial (DVB-T) with MPEG-2 video codec. Because the digital video has to be transmitted by 60Hz power signal, interlace scanned video fields are transmitted instead of progressive video frames. The de-interlacing processing thus becomes one important control unit in digital television. It would transform the interlaced video to progressive type video for display on LCD-TV or PDP-TV. However, it need high-speed memory to buffer store several video fields for performing de-interlacing. We proposed to combine entropy coding with the de-interlacing algorithm to reduce the size of the memory buffer for cost down. The most prominent feature of this method is its adaptation capability. When the buffer memory requirement in compressing exceeds the pre-defined capacity, it would adapt its coding strategy to distribute the coding error into the whole frame, such that acceptable video quality is guaranteed. Simulations demonstrate that the proposed method can provide both subjective and objective coding and de-interlacing performances.

摘要 I
Abstract II
誌謝 III
圖表目錄 VI
第一章 研究背景與文獻探討 1
1.1研究背景 1
1.2研究動機與目的 1
1.3文獻回顧 4
1.4論文架構 5
第二章 資料壓縮原理 6
2.1 色彩轉換 6
2.2 MPEG-2簡介 7
2.3 取樣格式 7
2.4 Self-Information 9
2.5熵與熵編碼(Entropy and Entropy Coding) 10
2.5.1熵(Entropy) 10
2.5.2 熵編碼(Entropy Coding) 11
2.6 霍夫曼編碼(Huffman Coding) 13
2.6.1 霍夫曼編碼簡介 13
2.6.2霍夫曼編碼程序 13
2.6.3霍夫曼編碼實例 13
2.7預測編碼 15
2.7.1預測編碼之介紹 15
2.7.2差分訊號編碼(DPCM) 15
第三章 解交錯演算法 18
3.1交錯、非交錯掃瞄簡介 18
3.2解交錯演算法 20
3.2.1空間域方法 20
3.2.2時間域方法 23
3.2.3 Spatio-Temporal Edge-Based Median Filtering 24
第四章 結合霍夫曼壓縮與STELA演算法 26
4.1簡介 26
4.2 系統架構 26
4.3影像的壓縮編碼 30
4.3.1 DPCM 編碼 30
4.3.2 霍夫曼編碼(Huffman coding) 32
4.4解壓縮處理(decoding process) 34
4.4.1 霍夫曼解碼(Huffman decoding) 34
4.4.2 DPCM 解碼 37
4.5 解交錯STELA演算法 38
4.5.1 Cb、Cr 線性化 38
4.5.2 STELA演算法 38
第五章 實驗結果與討論 41
5.1影像品質評估 41
5.2實驗結果討論 41
第六章 結論與未來的研究方向 67
6.1結果與討論 67
6.2未來研究方向 67
參考文獻 69
附錄A 71

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