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Empirical Analysis of Video Partitioning Methods for Distributed HEVC Encoding

초록

영어

As cloud computing has emerged as a promising technique in mainstream application domains, significant attention has been paid to distributed video encoding, in which resource-intensive encoding tasks are distributed across unlimited computational resources available in the cloud environment. For distributed video encoding, the input video must be partitioned into several segments. This approach decreases the total encoding time but may suffer from quality degradation associated with a lack of information, such as the coding complexity of the previous video segment. In this paper, two well-known video partitioning methods are explored from different performance perspectives, including encoding time, bitrates, and peak signal-to-noise ratio (PSNR).

목차

Abstract
 1. Introduction
 2. Related Work
 3. MapReduce-based Distributed HEVC Encoder
 4. Video Partitioning Methods
  4.1. Uniform Partitioning
  4.2. GOP-based Partitioning
 5. Empirical Analysis of Video Partitioning Methods
 6. Conclusion and Future Work
 Acknowledgements
 References

저자정보

  • Byoung-Dai Lee Department of Computer Science, Kyonggi University, Suwon, Korea

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