身為國際最新的影音編碼標準，H.264不管是在桌上型電腦或是手持裝置上的重要性都與日俱增。對於手持裝置來說，系統能源的消耗極為重要，必須從系統設計的各個層面來探討。在軟體應用層上，程式碼大小影響了記憶體的階層行為，也間接影響了系統的耗能表現。在本論文中，我們考慮在使用了多核心訊號處理器的手持裝置上執行H.264的能量消耗，並專注在透過程式碼減縮來達到節省耗能的目的。我們以多重「畫面中亮度預測」模式來當例子。在H.264中，對於每一個4 × 4畫面中亮度區塊，擁有達九種之多的預測模式可供選擇，需要九種不同的程序來完成。我們細查了這些預測模式的存取型態，提出了一個適用一般性的解決方案，將原來九種中的八種程序以一個新的程序取代。這個解決方案戲劇性地減縮了程式碼的大小並達到減低能源消耗的結果。我們在PACDSP上實做這份程式碼，程式碼減縮程度達到20%，並將系統效能的降低控制在可忽略的範圍內。

As the latest international video coding standard, H.264 is gaining importance not only on desktop computers but also on handheld devices. For handheld devices, power consumption of the system is of ultimate importance, which needs to be addressed at every layer of the system design. At the application layer, the code size affects the memory hierarchy behavior, which in turn affects the system power performance. In this thesis, we consider the power consumption of H.264 running on handheld devices using multicore DSP processors, focusing particularly on power saving through code reduction. We use the multiple intra-mode prediction as an example. There are nine optional prediction modes for each 4 × 4 intra luma sub-macroblock in H.264, which result in nine separate routines. We examine the access patterns of the modes and propose a general scheme that replaces eight of the nine routines with one. This dramatically reduces the code size and the resultant power consumption. We implemented the code on PACDSP, resulting in a 20% reduction of the code size with negligible performance degradation.

Abstract...................................I
Table of Contents ........................II
List of Figures .........................III
List of Tables ...........................IV
Chapter 1 Introduction ...........................................1
Chapter 2 Background ...........................................5
2.1. The 4 × 4 Intra Luma Prediction ...........................................5
2.2. Target Architecture ...........................................8
2.3. Opportunities and Constraints...............................11
Chapter 3 Proposed Scheme ..........................................14
3.1. Prediction Data Calculation ..........................................14
3.2. Prediction Sample Retrieval ..........................................19
Chapter 4 Implementation and Results ..........................................22
4.1. POP Version and Full Version ..........................................22
4.2. Performance Oriented Prediction (POP Version) ......................................... 22
4.3. Full Version ..........................................25
Chapter 5 Conclusion ..........................................27
Reference ................................28
Appendix A ...............................30
(Illustrative Reference for PACDSP v3.3) ..........................................30

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