臺灣博碩士論文加值系統

這篇論文的主要目標是為低位元率輕量級視訊壓縮設計一種熵編碼模式切換演算法。在低位元率輕量級視訊壓縮中，熵編碼為系統中最花費時間的步驟。而熵編碼採用CABAC平均約需要CAVLC 1.5倍的時間。所以我們發展熵編碼模式切換演算法以達到有效切換模式的目的。我們利用熵編碼模式切換演算法計算在使用位元率控制下，單張畫面使用兩種熵編碼模式會造成的畫質差。當CABAC能比CAVLC能有效提升畫面達0.5dB以上，才使用CABAC模式，反之則使用CAVLC以節省時間。在演算法中，我們同時也為CABAC發展碼率預估演算法而能用更快的速度取得CABAC的壓縮量。從模擬結果顯示能比只用CAVLC模式平均每張畫面能達到0.7dB的畫質提昇，而相對CABAC比CAVLC需要的時間，使用切換演算法平均能節省40%的時間。

The objective of this thesis is to develop a dual-mode entropy coding selection algorithm for the low bit-rate lightweight video compression system. In applications of low bit-rate lightweight video compression system, entropy coding stage costs the most time and the time of CABAC is 1.5 times the time of CAVLC on the average. As a result, we develop a dual-mode entropy coding selection algorithm to choose the better mode for each frame encoded. Dual-mode entropy coding selection algorithm can be used to compute the PSNR difference of one frame which encoded by two kinds of entropy coding mode under rate control. When CABAC increases better PSNR value than CAVLC by 0.5 dB, the algorithm chooses CABAC mode to encode. When the value is below the threshold, the algorithm selects another mode. In our algorithm, we also present a CABAC rate estimation method to save computation complexity. Simulations shows that the proposed algorithm can produce better PSNR value by 0.7 dB than encoding by CAVLC only and save up to 40% additional simulation time than encoding by CABAC only.

中文摘要 i
英文摘要 ii
誌 謝 iii
目 錄 iv
圖 目 錄 vi
表目錄 viii
第一章 緒論 1
1.1研究動機與目的 1
1.2 論文架構 5
第二章 背景介紹與文獻回顧 6
2.1 膠囊內視鏡取像格式 6
2.2 H.264/MPEG-4 AVC畫面內估測的壓縮演算法 7
2.2.1 畫面內估測 (Intra prediction) 7
2.2.2 整數轉換 (4×4 IntegerTransform) 10
2.2.3 量化 (Quantization) 12
2.3熵編碼 14
2.3.1內容適應性變動長度編碼法(CAVLC) 14
2.3.2內容適應性二元算術編碼(CABAC) 17
2.3.2.1 二元轉換 18
2.3.2.2 基於內容的機率模型選擇 19
2.3.2.3 適應性二元算術編碼 21
2.4 碼率預估 (rate estimation) 的文獻回顧 26
2.5 位元率控制 (rate control) 29
第三章 熵編碼模式切換演算法的設計 30
3.1畫面內估測模式的簡化 30
3.1.1 DC模式 31
3.1.2 重建G1模式 31
3.2 熵編碼模式切換演算法 32
3.2.1 畫面編碼預算的計算 36
3.2.2 QP的計算 39
3.2.3 PNSR差值的計算 41
3.2.4 模型係數更新 42
3.2.5 線性模型的硬體實現分析 43
3.3 熵編碼的碼率預估演算法 46
3.3.1 CAVLC碼率預估演算法 47
3.3.2 CABAC碼率預估演算法 53
3.3.2.1 初步的CABAC碼率預估演算法 53
3.3.2.2 改良的CABAC碼率預估演算法 58
3.3.2.3 位元平行化碼率預估演算法 61
3.3.2.4 level查表碼率預估演算法 64
3.3.2.5 各種方法的優劣比較 66
第四章 熵編碼模式切換演算法模擬結果 71
4.1 演算法模擬結果 71
4.2 硬體成本模擬結果 74
第五章 結論 75
參考文獻 76

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