本論文針對CGS-SVC提出一個結合統計和線性關係的混合模型來對可調視訊編碼進行解碼複雜度預測，其主要是透過一個線性關係，進而顯著地降低只單純使用統計方法預測的計算複雜度。除此之外，我們更進一步地將預測模型和影像層選擇機制結合，形成一個可以根據平台運算能力變化而進行不同複雜度解碼的運算能力感知解碼架構。由實驗結果可知，我們所提出的混合模型在各品質層和不同的影像複雜度下，都能提供一個精確而穩定的預測結果，相較於線性模型有更好預測精準度，平均預測誤差為1.51%。在預測解碼複雜度的Overhead Complexity只有2.1%，約統計模型的五分之一。我們提出的運算能力感知解碼架構，可在平台運算能力下降時，正確的選擇較低複雜度的影像層。經由和一個沒有預測機制的情況做比較，証實我們的解碼架構，在運算能力改變不斷改變的情況下，仍可保持在一個較小的PSNR變化。

This paper presents a hybrid model which combines a statistic model with a linear relationship to predict CGS-SVC decoding complexity. This model decreases the computing complexity of the statistic model evidently by the linear method. Furthermore, we integrate our model with the layer decision mechanism to form the computation-aware decoding architecture which can adjust decoding complexity according to the computing power of the target platform. In experimental result, the prediction error is 1.51% and overhead complexity is 2.1% for our proposed. It means the model provides not only a more accurate and stable prediction than linear model but also an one-fifth of overhead complexity for the statistic model. The experiment also show our architecture chose a suitable decoding complexity correctly in a computing power descending situation and keeping a smaller variation of PSNR compares to the decoding system without our mechanism.

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