畫質控制在視訊編碼中是一個很重要的議題，本研究提出一個根據畫面內容分析來動態調整編碼參數的方式，讓壓縮視訊達到品質恆定的需求。我們利用了畫面縮放與奇異值分解的方式來判定畫面複雜度，藉由訓練，我們將複雜度相對應至失真模型參數，以便選擇適當的量化參數。我們另也採用較簡單的畫面間預測來協助P畫面中的複雜度計算。我們所提出的架構可以被使用於不同的品質衡量標準，例如PSNR或SSIM。為了獲得更精確的恆定品質畫面，我們使用了兩種編碼，一種是當畫面變動過大時，採用編碼兩次的方式，第一次經由統計模型預估編碼參數，第二次以第一次編碼的結果來更新統計模型進行編碼。另一種則是畫面變動較小時，我們根據之前畫面的編碼結果更新預估編碼參數模型。實驗結果顯示我們提出的方式對於各種不同的影片皆可以達到恆定品質效果。

Quality control is important in video coding, which tries to dynamically adjust the encoder parameters for achieving the target distortion. In this thesis, we propose a quality control framework for the constant quality coding in H.264/AVC. The proposed scheme can assign a suitable Quantization Parameter (QP) to each frame based on the scene complexity. In intra-coded frames, we evaluate the scene complexity based on the quality measurements of the resized and singular value decomposition processed frames. With the proposed model, we can adjust the QP to achieve the target distortion. Our propose framework can use different quality measurements such as Peak Signal to Noise Ratio and Structural Similarity. For inter-coded frames, we employ the additional temporal information by the simple motion estimation to improve the prediction accuracy. We also propose a dynamic encoding mechanism for the model adjustment. When the content has large variations, we may encode the frame twice. Otherwise, we encode it only once. In addition, the effect of scene changes on the model update is also considered to reduce the quality deviation from the target. Experimental results show that our scheme performs well in various test videos.

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