Performance and energy consumption analysis of the X265 video encoder

Silveira, Dieison; Porto, Marcelo; Bampi, Sérgio

doi:10.23919/eusipco.2017.8081463

Cited by 14 publications

(3 citation statements)

References 8 publications

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“…3) Encoding parameters selection: We select 19 bitrates from 100 kbps to 20 Mbps (see Table I) and nine encoding presets that define the quality to encoding speed ratio: ultrafast, superfast, veryfast, faster, fast, medium (default preset), slow, slower, and veryslow [6]. A slower preset uses more features for the same bitrate, which leads to a relatively slower encoding speed and better video quality [29]. Similarly, faster presets produce lower video quality.…”

Section: A Encoding Data Generationmentioning

confidence: 99%

Where to Encode: A Performance Analysis of x86 and Arm-based Amazon EC2 Instances

Mathá¹,

Kimovski²,

Zabrovskiy³

et al. 2021

Preprint

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Video streaming became an undivided part of the Internet. To efficiently utilise the limited network bandwidth it is essential to encode the video content. However, encoding is a computationally intensive task, involving high-performance resources provided by private infrastructures or public clouds. Public clouds, such as Amazon EC2, provide a large portfolio of services and instances optimized for specific purposes and budgets. The majority of Amazon's instances use x86 processors, such as Intel Xeon or AMD EPYC. However, following the recent trends in computer architecture, Amazon introduced Armbased instances that promise up to 40% better cost performance ratio than comparable x86 instances for specific workloads. We evaluate in this paper the video encoding performance of x86 and Arm instances of four instance families using the latest FFmpeg version and two video codecs. We examine the impact of the encoding parameters, such as different presets and bitrates, on the time and cost for encoding. Our experiments reveal that Arm instances show high time and cost saving potential of up to 33.63% for specific bitrates and presets, especially for the x264 codec. However, the x86 instances are more general and achieve low encoding times, regardless of the codec.

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Section: A Encoding Data Generationmentioning

confidence: 99%

Where to Encode: A Performance Analysis of x86 and Arm-based Amazon EC2 Instances

Mathá¹,

Kimovski²,

Zabrovskiy³

et al. 2021

Preprint

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“…Optimized encoding preset: Traditional open-source encoders like x264 [15], x265 [14], and VVenC [16] have pre-defined sets of encoding parameters (termed as presets), which present a trade-off between the encoding time and compression efficiency [7,17]. The preset for the fastest encoding (ultrafast for x264 and x265) is used as the encoder preset for the entire live content, independent of the video content complexity [10].…”

Section: Introductionmentioning

confidence: 99%

Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low latency Encoding

Menon,

Zhu,

Rajendran

et al. 2024

Proceedings of the 3rd Mile-High Video Conference on ZZZ

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In HTTP adaptive live streaming applications, video segments are encoded at a fixed set of bitrate-resolution pairs known as bitrate ladder. Live encoders use the fastest available encoding configuration, referred to as preset, to ensure the minimum possible latency in video encoding. However, an optimized preset and optimized number of CPU threads for each encoding instance may result in (i) increased quality and (ii) efficient CPU utilization while encoding. For low latency live encoders, the encoding speed is expected to be more than or equal to the video framerate. To this light, this paper introduces a Just Noticeable Difference (JND)-Aware Low latency Encoding Scheme (JALE), which uses random forest-based models to jointly determine the optimized encoder preset and thread count for each representation, based on video complexity features, the target encoding speed, the total number of available CPU threads, and the target encoder. Experimental results show that, on average, JALE yield a quality improvement of 1.32 dB PSNR and 5.38 VMAF points with the same bitrate, compared to the fastest preset encoding of the HTTP Live Streaming (HLS) bitrate ladder using x265 HEVC open-source encoder with eight CPU threads used for each representation. These enhancements are achieved while maintaining the desired encoding speed. Furthermore, on average, JALE results in an overall storage reduction of 72.70 %, a reduction in the total number of CPU threads used by 63.83 %, and a 37.87 % reduction in the overall encoding time, considering a JND of six VMAF points.

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“…This reduction in bitrate comes at the cost of an increase in computing demands on the server side. As a consequence, the burden is shifting from network pressure to compute requirements in terms of both performance and energy consumption [4]. Moreover, video streams often have to be converted to match the requirements of different clients by means of online video transcoding, which is a very resource intensive process [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

Costero

Iranfar

Zapater

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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The advent of online video streaming applications and services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher video quality and more compression at the cost of increased complexity. On one hand, HEVC exposes a set of dynamically tunable parameters to provide trade-offs among Quality-of-Service (QoS), performance, and power consumption of multi-core servers on the video providers' data center. On the other hand, resource management of modern multi-core servers is in charge of adapting system-level parameters, such as operating frequency and multithreading, to deal with concurrent applications and their requirements. Therefore, efficient multiuser HEVC streaming necessitates joint adaptation of application-and system-level parameters. Nonetheless, dealing with such a large and dynamic design space is challenging and difficult to address through conventional resource management strategies. Thus, in this work, we develop a multi-agent Reinforcement Learning framework to jointly adjust application-and system-level parameters at runtime to satisfy the QoS of multiuser HEVC streaming in power-constrained servers. In particular, the design space, composed of all design parameters, is split into smaller independent sub-spaces. Each design sub-space is assigned to a particular agent so that it can explore it faster, yet accurately. The benefits of our approach are revealed in terms of adaptability and quality (with up to to 4× improvements in terms of QoS when compared to a static resource management scheme), and learning time (6× faster than an equivalent mono-agent implementation). Finally, we show that the power-capping techniques formulated outperform the hardware-based power capping with respect to quality.

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Performance and energy consumption analysis of the X265 video encoder

Cited by 14 publications

References 8 publications

Where to Encode: A Performance Analysis of x86 and Arm-based Amazon EC2 Instances

Where to Encode: A Performance Analysis of x86 and Arm-based Amazon EC2 Instances

Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low latency Encoding

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

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