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研究生:李俊儒
研究生(外文):Chun-Ju Li
論文名稱:基於梯度預測與投票策略之非失真影像編碼系統
論文名稱(外文):Gradient-adapted Prediction with Voting Strategy for Lossless Coding of Images
指導教授:高立人高立人引用關係
指導教授(外文):Lih-Jen Kau
口試委員:蔣欣翰陳彥霖范育成
口試日期:2016-07-27
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電子工程系碩士班(碩士在職專班)
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:104
語文別:中文
中文關鍵詞:投票策略誤差建模非失真影像編碼梯度預測
外文關鍵詞:Voting strategyContext modelingLossless image codingGradient prediction
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本論文提出以梯度預測之方式應用於非失真影像編碼,目的是去除影像中的冗餘性藉以減少資料容量。然而,壓縮方法的優劣與被壓縮資料本身之特性有著直接的關係,為了降低預測所需之運算量,我們提出低複雜度的梯度投票預測演算法,依據預測像素點的周圍三點來建構資訊,判別編碼像素之紋理走向,透過鄰近的環境進行多數決策略展開梯度偵測。梯度投票預測器在紋理明顯變化區域能得到較高的編碼效率,而啟用二元編碼在像素值緩慢變化的區域則是非常有用的編碼方式。
因此,我們提出結合梯度投票預測以及二元編碼這兩種模式的優點。在影像緩慢變化區域中的像素,使用二元模式編碼,其他區域則使用一般模式梯度投票的預測。我們提出的梯度投票方法是依據相鄰像素點統計的梯度及強度之比例,分配編碼像素點之權重,致使實際位元率可以被顯著地降低。誤差建模中複雜的估測統計係數已經事先離線訓練確認,預測像素編碼沒有額外的輔助訊息需要被傳輸。本論文所提出的方法可以顯著減少複雜性與資訊量,能夠獲得計算複雜度和預測結果之間的良好平衡。經實驗證明,所提出之演算法與現有相同類型的預測技術比較,預測器和編碼器都顯示出我們提出的系統優越性。
This paper proposes an algorithm applied to the lossless image compression by gradient prediction method. We want to reduce the data bulk by decreasing the image redundancy. However, data characteristics which were compressed affect compressing performance directly. In order to reduce the prediction loading, we proposed a simple gradient vote prediction algorithm. We construct the information by predicting three pixels around the current pixel, and classify texture trend of prediction unit. The majority decision and boundary detection are made with characteristics of neighbor pixels. Our gradient vote texture predictors can get higher coding efficiency in obviously changes boundary area in the image. On the other hand, binary mode encoding is a useful method to deal with the area which image pixels change slowly. In this thesis, we combine both advantages of gradient vote prediction and binary mode encoding. We use binary mode encoding when the image pixels of the area change slowly. Otherwise, we select regular mode to encode. Our gradient vote method is according to a proportional of the adjacent pixels statistics and strength to allocate the weight of the encoding pixels leads computational complexity is significantly reduced. However, complexly statist and estimate coefficient in error modeling has been offline trained and confirmed, no additional auxiliary information needs to be transmitted in coding prediction pixel. Our algorithm can significantly reduce the complexity and amount of information. It is able to obtain a well trade-off between computational complexity and prediction outcomes. The experiments depict that the proposed algorithm of the same type of comparison with the current forecasting techniques in prediction and encoders have shown the superiority of our proposed system.
中文摘要 i
英文摘要 iii
誌謝 v
目錄 vi
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 文獻回顧 3
1.4 論文架構 5
第二章 系統架構與模式介紹 6
2.1 系統架構 6
2.2 二元編碼模式 9
2.3 一般模式 10
2.4 熵編碼 11
2.5 編譯軟體介紹 12
第三章 梯度測邊與投票決策演算法 13
3.1 梯度預測原理 14
3.2 梯度誤判分析 17
3.3 投票機制 19
3.4 方法分析 20
3.5 整體流程 26
第四章 預測誤差補償編碼 27
4.1 誤差補償 28
4.1.1 編碼環境 28
4.1.2 環境結構 32
4.1.3 誤差反饋 34
4.1.4 整體誤差補償流程 34
4.2 誤差補償細緻化 35
4.2.1 誤差正負號翻轉 35
4.2.2 重編誤差符號 36
4.2.3 誤差符號截斷 39
第五章 實驗結果 41
5.1 實驗環境 41
5.2 二元編碼模式分析 43
5.3 預測器效能驗證與比較 50
5.4 誤差補償效能驗證 54
5.5 編碼器效能驗證與比較 63
5.6 運算複雜度 68
第六章 結論及未來展望 69
6.1 結論 69
6.2 未來展望 70
參考文獻 71
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