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Parallel Scalability and Efficiency of HEVC Parallelization Approaches

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. Outline. Introduction

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Parallel Scalability and Efficiency of HEVC Parallelization Approaches

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  1. Parallel Scalability and Efficiency ofHEVC Parallelization Approaches Chi Ching Chi, Mauricio Alvarez-Mesa,, Ben Juurlink, Gordon Clare, F´elix Henry, St´ephanePateux 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.

  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 • Environment

  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% higherthan 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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