ABSTRACT
This paper presents a novel decoding-workload-aware video encoding scheme. It takes raw video data and decoding workload constraint of a mobile client as input and generates a video bitstream which matches such a constraint while striving to achieve the best video quality. For a given constraint, the best overall video quality of the encoded bitstream is selected with a tradeoff between spatial and temporal distortions. The main contributions of this paper include: 1) the proposal of an efficient scheme which selects the most suitable target frame rate before the actual encoding; 2) The design of a workload control (analogous to the rate control) scheme which ensures an accurate control of the decoding workload when the bitstream is generated using the proposed encoding scheme. Experimental results demonstrate the feasibility and performance of the proposed scheme.
- Y. Huang, V. Tran, Y. Wang, "A Workload Predication Model for Decoding MPEG Video and its Application to Workload-scalable Transcoding", ACM Multimedia Conference, pp. 952--961, September, 2007. Google ScholarDigital Library
- M. Bonuccelli, F. Lonetti, F. Martelli, "Temporal Transcoding for Mobile Video Communication", the second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, pp. 18--29, March, 2005. Google ScholarDigital Library
- K. Ngan, T. Meier, Z. Cheng, "Improved Single-video Object Rate Control for MPEG-4", Circuits and Systems for Video Technology, IEEE Transactions on, pp. 385--393, May, 2003. Google ScholarDigital Library
- http://tcpmp.corecodec.org.Google Scholar
- T. Austin, E. Larson, D. Ernst, "Simplescalar:An infrastructure for computer system modeling", IEEE Computer, pp. 59--67, 2002. Google ScholarDigital Library
Index Terms
- Decoding-workload-aware video encoding
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