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Article dans une revue IEEE Transactions on Image Processing Année : 2017

Rate Allocation in Predictive Video Coding Using a Convex Optimization Framework

Résumé

Optimal rate allocation is among the most challenging tasks to perform in the context of predictive video coding, because of the dependencies between frames induced by motion compensation. In this paper, using a recursive rate-distortion model that explicitly takes into account these dependencies, we approach the frame-level rate allocation as a convex optimization problem. This technique is integrated into the recent HEVC encoder, and tested on several standard sequences. Experiments indicate that the proposed rate allocation ensures a better performance (in the rate-distortion sense) than the standard HEVC rate control, and with a little loss w.r.t. an optimal exhaustive research which is largely compensated by a much shorter execution time.
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Dates et versions

hal-01718496, version 1 (27-02-2018)

Identifiants

Citer

Aniello Fiengo, Giovanni Chierchia, Marco Cagnazzo, Béatrice Pesquet-Popescu. Rate Allocation in Predictive Video Coding Using a Convex Optimization Framework. IEEE Transactions on Image Processing, 2017, 26 (1), pp.479 - 489. ⟨10.1109/TIP.2016.2621666⟩. ⟨hal-01718496⟩
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