H.265 (或稱HEVC)是2013年ISO通過的新一代的視訊與影像壓縮技術標準，與前一代壓縮標準H.264相比可以獲得50%的壓縮效率提升，並且可以支援最高畫素8K x 4K的影片壓縮。H.265壓縮的概念是將一張畫面切成數個Coding Unit (CU)以z-scan的方式依序作壓縮；在H.265的標準下，提出了Coding Tree的概念。Coding Tree是以Coding Unit, Predict Unit, Transform Unit所組成，為了提高壓縮後影像的品質，將64x64的CU再細分成32x32, 16x16, 8x8總共四層。除此之外，H.265也提出多項新的機制，目的是增加壓縮的影像品質和降低儲存所需要的位元；但伴隨而來的是複雜的運算以及大量的運算時間需求。本論文研究探討運用雲端平行計算技術來獲取有效率的加速效果，達到編碼速度的提升。
在H.265標準規畫三種平行計算概念：Slices, Tiles, 及Wave-front。Slice-based的方法，主要作法是把一張影像切成數個彼此間獨立的Slices，再平行處理同步壓縮。本篇論文提出了以slice-based為基準雲端平台來達到平行計算目的。均勻切割式slice-based的方法存在運算量變異大的情況，本論文提出利用預測運算量來切割Slices以期達到運算量平衡，讓加速效果(Speedup)有效提升。Wave-front的方法主要是讓影像分成彼此間一樣大但並不獨立的wave，根據資料的關聯性依序作壓縮。由於Wave-front執行時會因為wave之間具有相關性，需要互相傳遞相關資料而經常造成等待(Delay)的情況；本篇論文提出了一套方法可以減少Delay，讓加速效果(Speedup)提升。

The H.265 (high efficiency video coding, HEVC) standard, approved April 2013, needs only half the bit rate of its predecessor, the H.264, which remains the most deployed video compression standard worldwide. H.265 can support screen resolutions ranging from 320 × 240 to 8K × 4K. The basic step of video compression is dividing a frame into plenty of coding units (CUs) and encoding in the zigzag scan order. Instead of H.264 macro-blocks, H.265 proposes a coding tree structure consisting of coding units (CUs), predict units (PUs) and transform units (TUs). For example, a 64x64 CU can be divided into four 32x32 CUs, a 32x32 CU can be divided into four 16x16 CUs and a16x16 CU can be divided into four 8x8 CUs. Furthermore, several new techniques are also proposed so as to further decrease the resulting bit-rate to double the coding efficiency than that of H.264. Apparently, the side effect of perceivable tremendous computational complexity should be solved by all means. In this thesis, cloud computing approaches to accelerate the speedup of the execution time of H.265 encoding are proposed with little bit-rate increasing and PSNR decreasing.
Three parallel processing modes are suggested in H.265, naming slices, tiles, and wave-front. Both the slice-based and wave-front-based are considered and realized in parallel in this thesis. In the slice-based encoding, a frame is divided into several slices which are assuming independent to each other while encoding. Generally, load balancing is a challenge problem; a workload estimation and allocation method is proposed to balance the workload on the fly. As for the wave-front solution, it splits a frame into CU rows; dependences between rows should be synchronized and maintained carefully. To improve the efficiency of parallel processing, not only load balancing is a challenge, but the delays of synchronization must be as small as possible. Experiments conducted on 7-16 virtual machines system show that our implementations achieve better speedups, for slice-based, wave-front-based, respectively.

[1] G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, “Overview of the High Efficiency Video Coding (HEVC) standard,” IEEE Transaction Circuits Systems for Video Technology, vol. 22, no. 12, pp.1648–1667, December 2012.

[2] J.-R. Ohm, G. J. Sullivan, H. Schwarz, T.-K Tan, and T. Wiegand, “Comparison of the Coding Efficiency of Video Coding Standards—Including High Efficiency Video Coding,” IEEE Transaction Circuits Systems for Video Technology, vol. 22, no. 12, pp. 1669–1684, December 2012.

[3] F. Bossen, B. Bross, K. Suehring, and D. Flynn, “HEVC Complexity and Implementation Analysis,”IEEE Transaction Circuits Systems for Video Technology, Vol. 22, No. 12, pp. 1685‒1696,December 2012.

[4] Pierre Andrivon, Marco Arena, Philippe Salmon, Philippe Bordes, Paola Sunna, "Comparison of Compression Performance of HEVC Draft 10 with AVC for UHD-1 material,"DocumentJCTVC-L1003_v34ofJoint Collaborative Team on Video Coding, April 2013.

[5] H. I-Chien, “Load Balanced Slice-level Parallelism of the HEVC Encoder,” Master’s Thesis, Department of Computer Science, National Tsing Hua University, July 2012.

[6] Kiho Choi, Sang-Hyo Park, and Euee S. Jang,” Coding tree pruning based CU early termination,” documentJCTVC-F092ofJoint Collaborative Team on Video Coding, July 2011.

[7] Peiyin Xing, YonghongTian, Tiejun Huang, Wen Gao,” Surveillance Video Coding with Quadtree Partition Based ROI Extraction,” Picture Coding Symposium (PCS), December2013.

[8] H Kuang-Li, “Automatic Fast Forwarding for Surveillance Video using Saliency Detection,” Master’s Thesis, Department of Computer Science, National Tsing Hua University,July 2011.

[9] Chi Ching Chi, Alvarez-Mesa M., Juurlink B., Clare G., Henry F., Pateux S., Schierl T.,” Parallel Scalability and Efficiency of HEVC Parallelization Approaches,” IEEE Transaction Circuits Systems for Video Technology, vol. 22, no. 12, pp. 1827–1838, December 2012.

[10] ISO/IEC JVC 1 SC29 WG11,” Joint Call Proposals on Video Compression Technology,” documentN11113ofJoint Collaborative Team on Video Coding, January 2012.

[11] Song Lin, Xinfeng Zhang, Qin Yu, HonggangQi,Siwei Ma ” Parallelizing Video Transcoding with Load Balancing on Cloud Computing,” IEEE Transaction Circuits and Systems(ISCAS), May 2013.

[12] Hsu-Feng Hsiao, Chen-Tsang Wu, "Balanced Parallel Scheduling for Video Encoding with Adaptive GOP Structure,” IEEE Transaction Parallel and Distributed System, p2355-2364, May 2013.

[13] Gu Junli, Sun Yihe, "Optimizing a Parallel Video Encoder with Message Passing and a Shared Memory Architecture,”Tsing Hua Science and Technology, p393-398, August2011.

[14] Kyungmin Lim, Seongwan Kim, Jaeho Lee, Daehyun Pak, Sangyoen Lee, "Fast Block Size and Mode Decision Algorithm for Intra Prediction in H.264/AVC,”IEEE Transaction Consumer Electronics,vol. 58, no. 2, p654-660, May2012.