Low-power high-efficiency video decoding using general purpose processors

In this article, we investigate how code optimization techniques and low-power states of general-purpose processors improve the power efficiency of HEVC decoding. The power and performance efficiency of the use of SIMD instructions, multicore architectures, and low-power active and idle states are analyzed in detail for offline video decoding. In addition, the power efficiency of techniques such as “race to idle” and “exploiting slack” with DVFS are evaluated for real-time video decoding. Results show that “exploiting slack” is more power efficient than “race to idle” for all evaluated platforms representing smartphone, tablet, laptop, and desktop computing systems

Toward Free and Open Source Film Projection for Digital Cinema

International audienceCinema industry has chosen Digital Cinema Package (DCP) as encoding format for the distribution of digital films. DCP uses JPEG2000 for video compression. An efficient implementation of coding and decoding for this format is complex, however. Currently deployed equipment is expensive and has high maintenance costs, preventing art-house cinema theaters from acquiring it. Therefore, we conduct this research activity in cooperation with Utopia cinemas, a group of art-house cinemas, whose main requirement (besides functional ones) is to provide Free and Open Source Software (FOSS). This paper presents a solution that achieves real-time JPEG2000 decoding and DCP presentation based on widespread open source multimedia tools, namely VLC and libavcodec library. We present the improvements that were made in VLC to support the DCP packaging format, as well as details on JPEG2000 decoding inside libavcodec (optimization and lossy decoding). We also evaluate the performance of the decoding chai

Multi-user video streaming using unequal error protection network coding in wireless networks

In this paper, we investigate a multi-user video streaming system applying unequal error protection (UEP) network coding (NC) for simultaneous real-time exchange of scalable video streams among multiple users. We focus on a simple wireless scenario where users exchange encoded data packets over a common central network node (e.g., a base station or an access point) that aims to capture the fundamental system behaviour. Our goal is to present analytical tools that provide both the decoding probability analysis and the expected delay guarantees for different importance layers of scalable video streams. Using the proposed tools, we offer a simple framework for design and analysis of UEP NC based multi-user video streaming systems and provide examples of system design for video conferencing scenario in broadband wireless cellular networks

DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs

In addition to General Purpose Processors (GPP), Multicore SoCs equipping modern mobile devices contain specialized Digital Signal Processor designed with the aim to provide better performance and low energy consumption properties. However, the experimental measurements we have achieved revealed that system overhead, in case of DSP video decoding, causes drastic performances drop and energy efficiency as compared to the GPP decoding. This paper describes DyPS, a new approach for energy-aware processor switching (GPP or DSP) according to the video quality . We show the pertinence of our solution in the context of adaptive video decoding and describe an implementation on an embedded Linux operating system with the help of the GStreamer framework. A simple case study showed that DyPS achieves 30% energy saving while sustaining the decoding performanc

Evaluation of the Performance/Energy Overhead in DSP Video Decoding and its Implications

Video decoding is considered as one of the most compute and energy intensive application in energy constrained mobile devices. Some specific processing units, such as DSPs, are added to those devices in order to optimize the performance and the energy consumption. However, in DSP video decoding, the inter-processor communication overhead may have a considerable impact on the performance and the energy consumption. In this paper, we propose to evaluate this overhead and analyse its impact on the performance and the energy consumption as compared to the GPP decoding. Our work revealed that the GPP can be the best choice in many cases due to the a significant overhead in DSP decoding which may represents 30% of the total decoding energy

Statistical framework for video decoding complexity modeling and prediction

Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time - feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding