FAST rate allocation through steepest descent for JPEG2000 video transmission

Francesc Aulí-Llinàs; Ali Bilgin; Michael W. Marcellin

doi:10.1109/TIP.2010.2077304

FAST rate allocation through steepest descent for JPEG2000 video transmission

Francesc Aulí-Llinàs, Ali Bilgin, Michael W. Marcellin

Research output: Contribution to journal › Article › Research › peer-review

15 Citations (Scopus)

Abstract

This work addresses the transmission of pre-encoded JPEG2000 video within a video-on-demand scenario. The primary requirement for the rate allocation algorithm deployed in the server is to match the real-time processing demands of the application. Scalability in terms of complexity must be provided to supply a valid solution by a given instant of time. The FAst rate allocation through STeepest descent (FAST) method introduced in this work selects an initial (and possibly poor) solution, and iteratively improves it until time is exhausted or the algorithm finishes execution. Experimental results suggest that FAST commonly achieves solutions close to the global optimum while employing very few computational resources. © 2011 IEEE.

Original language	English
Article number	5582283
Pages (from-to)	1166-1173
Journal	IEEE Transactions on Image Processing
Volume	20
Issue number	4
DOIs	https://doi.org/10.1109/TIP.2010.2077304
Publication status	Published - 1 Apr 2011

Keywords

Motion JPEG2000
rate allocation
rate-distortion optimization
video transmission

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title = "FAST rate allocation through steepest descent for JPEG2000 video transmission",

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AB - This work addresses the transmission of pre-encoded JPEG2000 video within a video-on-demand scenario. The primary requirement for the rate allocation algorithm deployed in the server is to match the real-time processing demands of the application. Scalability in terms of complexity must be provided to supply a valid solution by a given instant of time. The FAst rate allocation through STeepest descent (FAST) method introduced in this work selects an initial (and possibly poor) solution, and iteratively improves it until time is exhausted or the algorithm finishes execution. Experimental results suggest that FAST commonly achieves solutions close to the global optimum while employing very few computational resources. © 2011 IEEE.

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