隨著視訊處理技術的進步，視訊編碼品質也相對提升，然而編碼及傳送高畫質高影值視訊隨之而來的問題就是高頻寬高運算，為了有效降低位元率同時保持視訊低失真，HEVC(High Efficient Video Coding)於2013 年4月13日由ITU-T 推薦為正式視訊編碼標準，與前一代視訊編碼標準(H.264/AVC)相較，在相同的編碼品質下可達到兩倍之壓縮率，視訊解析度範圍從4K Ultra HD 至8K。 HEVC 考慮平行處理是未來趨勢，因此提供許多可平行工具予程式開發者使用，例如Wavefront Parallel Processing(WPP)、Tile、Entropy Slice 等。此外HEVC 有編碼單位(Coding Unit, CU)、預測單位(Prediction Unit,PU)以及轉換單位(Transform Unit, TU)等處理單元，相較於H.264/AV 的編碼單位(Macroblock, MB)大小固定為16×16 ，HEVC 的CU 大小可從64×64 至8×8，且支援非對稱分割預測，可有效降低位元率(bitrate)，然而許多的畫面分割和預測方法大幅地增加了計算複雜度。本論文根據HEVC 可高效平行處理機制及雲端平行運算特性，提出有效降低編碼時間的架構：(1) 將視訊分割並分配至雲端運算平台之不同節點以進行平行編碼，同時考慮了節點本身並行或加速編碼的可能性，可進一步延伸加速; (2)運算節點可藉由對同一張視訊畫面分割為多個矩形區域(Tiles)，運用CPU 多核心架構配合多執行緒平行編碼; (3) 藉由參考周圍或其他畫面的CU 以及PU 特性，提出二種快速演算法以節省不必要的CU 深度分割和PU 預測模式，進一步降低計算複雜度。利用本論文提出的編碼架構，可在少量的位元增加率和PSNR 損失下，平均可以減少94.813%的編碼時間。

With the advance of video coding technology, the resolution and quality of video frames have been largely improved. However, high resolution and high coding quality also imply high coding bitrate and higher time complexity. The high efficient video coding standard (HEVC), approved by ITU-T, in April 13, 2013, provide twice the compression efficiency, as compared to its previous version, H.264/AVC. The processing resolution range can be from 4K ultra HD up to 8192x4320. The HEVC was also designed to enable parallel processing to provide widespread coding platform applications, such as Wavefront Parallel Processing (WPP), independent tile coding, and entropy slice coding etc. In addition, the HEVC supports different processing units, such as Coding Unit (CU), prediction unit (PU) and transform unit (TU). In comparison with H.264/AVC, whose coding unit is fixed as 16 by 16, the HEVC supports CU from 64x64 to 8x8. The CU can be split asymmetrically to reduce encoding bit rate. However, these high efficient coding procedures also demand higher computation complexity. In this these, we proposed to improve the HEVC coding speed by utilizing cloud computing platform and multi-thread processing, in additional to developing early termination methods: (1) we segment the video into GOP units with different length to distribute to different cloud computing nodes; (2) The video coding can be speedup by parallel processing by dividing the image frame into tiles, such that multi-thread computing can encode them independently; (3) By referencing to surrounding CUs and Pus, the current CU depth decomposition and PU prediction can be early terminated to eliminate extra computational operations. In total, the proposed HEVC speedup coding methods can save 94.813% encoding time, while slightly increasing the bitrates and decreasing the PSNRs, as compared with the HM13.0 HEVC encoder description.

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