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Complexity Control for Low-Power HEVC Encoding

Abstract : The Internet of Things (loT) is now a reality. Forthcoming applications will boost mobile video demand to an unprecedented level. The induced increase in computational complexity is a challenge when executing in real-time new video coding standards on embedded platforms, limited in computing, memory, and energy. New 4K UHD and 360-degree video contents coming with high spatial (SK, 16K) and temporal (120fp resolutions further complicate the problem. In this context, codecs such as HEVC (High Efficiency Vide Coding) must be worked on to reduce their complexity while preserving the bitrate and image quality. Th bounded energy density of embedded system's batteries requires designers to propose new methods scaling and controlling the complexity and energy consumption of HEVC codecs. This document presents a set of studies aiming at scaling and controlling the complexity, and therefore the energy consumption, of HEVC Intra encoding. Two methods of quad-tree partitioning prediction in "one-shot are proposed: one based on variance-aware statistic approach and one based on Machine Learning using data-mining classifiers. From the obtained prediction, a generic tunable complexity scheme of HEVC encoding is introduced. It expands the search space around the original partitioning prediction and allocates complexit in a frame while minimizing performance loss in terms of bitrate and visual quality. Finally, a real-time contr system is created that dynamically manages the encoding process to keep the encoding complexity under specific tarjet. It demonstrates the a licability of the mayor contributions of this document.
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Submitted on : Sunday, February 2, 2020 - 1:05:19 AM
Last modification on : Friday, October 23, 2020 - 4:48:53 PM
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  • HAL Id : tel-02463835, version 1


Alexandre Mercat. Complexity Control for Low-Power HEVC Encoding. Signal and Image processing. INSA de Rennes, 2018. English. ⟨NNT : 2018ISAR0035⟩. ⟨tel-02463835⟩



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