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Parallel Scalability and Efficiency of HEVC Parallelization Approaches

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1 Parallel Scalability and Efficiency of HEVC Parallelization Approaches
Chi Ching Chi, Mauricio Alvarez-Mesa,, Ben Juurlink, Gordon Clare, F´elix Henry, St´ephane Pateux and Thomas Schierl IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

2 Outline Introduction Video codec parallelization approaches
Coding efficiency analysis Experimental evaluation Conclusions

3 Introduction While the single-core processor can decode a 1080p H.264/AVC video in real-time, it is very unlikely that processor performance will decode a 2160p50 HEVC video in real-time. To obtain real-time HEVC decoding performance, parallelism is no longer an option but a necessity.

4 Introduction H.264/AVC supports slice parallelization.
It may not achieve real-time if it receives a video with one or a few slices per frame. The main parallelization approaches currently included in the HEVC draft (Tiles and Wavefront Parallel Processing[WPP]). This paper presents a approach called Overlapped Wavefront(OWF).

5 Previous parallelization strategies
Frame-level parallelism Slice-level parallelism Macroblock-level parallelism

6 Frame-level parallelism
Frame-level parallelism consists of processing multiple frames at the same time. Frame-level parallelism is sufficient for multicore systems with just a few cores. If due to fast motion, motion vectors are long, there is little parallelism.

7 Slice-level Parallelism
Each frame can be partitioned into one or more slices. Slices in a frame are completely independent from each other and therefore they can also be used for parallel processing. It is useful for a frame with a few slices but not one slice per frame.

8 Macroblock-level Parallelism

9 Parallelization Strategies in HEVC
Tiles Wavefront Parallel Processing (WPP) Overlapped Wavefront (OWF)

10 Tiles

11 Tiles The number of tiles and the location of their boundaries can be defined for the entire sequence or changed from picture to picture. Compared to slices, Tiles have a better coding efficiency. The rate-distortion loss increases with the number of tiles. because Tiles allows picture partition shapes that contains samples with a potential higher correlation than slices

12 Wavefront Parallel Processing (WPP)

13 Overlapped Wavefront (OWF)
When a thread has finished a CTB row in the current picture and no more rows are available it can start processing the next picture instead of waiting for the current picture to finish. The support this approach, the motion vector is contrained to ¼ of picture height.

14 Overlapped Wavefront (OWF)

15 Coding efficiency analysis

16 Coding efficiency analysis

17 Experimental evaluation

18 Experimental evaluation

19 Experimental evaluation

20 Experimental evaluation

21 Experimental evaluation

22 Conclusions We present a detailed performance comparison of the main approaches, namely WPP ,Tiles and OWF. Tiles performance 7% higher than WPP on average at 12 cores. The proposed OWF 28% higher on average than Tiles. Achieve real-time performance for 1080p50 videos, but “only” 25.4 fps for 2160p.

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