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Decoder-Complexity-Aware Encoding of Motion Compensation for Multiple Heterogeneous Receivers

Published:24 February 2015Publication History
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Abstract

For mobile multimedia systems, advances in battery technology have been much slower than those in memory, graphics, and processing power, making power consumption a major concern in mobile systems. The computational complexity of video codecs, which consists of CPU operations and memory accesses, is one of the main factors affecting power consumption. In this article, we propose a method that achieves near-optimal video quality while respecting user-defined bounds on the complexity needed to decode a video. We specifically focus on the motion compensation process, including motion vector prediction and interpolation, because it is the single largest component of computation-based power consumption. We start by formulating a scenario with a single receiver as a rate-distortion optimization problem and we develop an efficient decoder-complexity-aware video encoding method to solve it. Then we extend our approach to handle multiple heterogeneous receivers, each with a different complexity requirement. We test our method experimentally using the H.264 standard for the single receiver scenario and the H.264 SVC extension for the multiple receiver scenario. Our experimental results show that our method can achieve up to 97% of the optimal solution value in the single receiver scenario, and an average of 97% of the optimal solution value in the multiple receiver scenario. Furthermore, our tests with actual power measurements show a power saving of up to 23% at the decoder when the complexity threshold is halved in the encoder.

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      • Published in

        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 11, Issue 2s
        Special Issue on MMSYS 2014
        February 2015
        138 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/2739966
        Issue’s Table of Contents

        Copyright © 2015 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 February 2015
        • Accepted: 1 November 2014
        • Revised: 1 October 2014
        • Received: 1 May 2014
        Published in tomm Volume 11, Issue 2s

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