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
키워드
Cloud ComputingDistributed Video EncodingHEVCMapReduce
저자
Byoung-Dai Lee [ Department of Computer Science, Kyonggi University, Suwon, Korea ]
보안공학연구지원센터(IJMUE) [Science & Engineering Research Support Center, Republic of Korea(IJMUE)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Multimedia and Ubiquitous Engineering
간기
월간
pISSN
1975-0080
수록기간
2008~2016
등재여부
SCOPUS
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Multimedia and Ubiquitous Engineering Vol.10 No.4