A Technical Overview of VP9—The Latest Open-Source Video Codec

Mukherjee, Debargha; Han, Jingning; Bankoski, Jim; Bultje, Ronald S.; Grange, Adrian; Koleszar, John; Wilkins, Paul; Xu, Yaowu

doi:10.5594/j18499

Cited by 95 publications

(45 citation statements)

References 3 publications

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“…AV1 employs an M-ary symbol arithmetic coding method that was originally developed for the Daala video codec [39] to compress the syntax elements, where integer M ∈ [2,14]. The probability model is updated per symbol coding.…”

Section: E N T R O P Y C O D I N G S Y S T E Mmentioning

confidence: 99%

A Technical Overview of AV1

et al. 2021

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Section: E N T R O P Y C O D I N G S Y S T E Mmentioning

confidence: 99%

A Technical Overview of AV1

et al. 2021

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“…It offered 12 videos at varying bitrates and spatial resolutions. [21] and AV1 [22] to enable interoperability testing and research in adaptation strategies for DASH clients with multiple video codecs. In another two studies by Bampis et al [14] and Duanmu et al [15], the authors conducted subjective QoE research with human viewers using their own DASH datasets.…”

Section: Background and Related Workmentioning

confidence: 99%

EmuStream—An End-to-End Platform for Streaming Video Performance Measurement

2020

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Cord-cutting has spread like wildfire as streaming video become commonplace in the Internet. This motivated intensive research in adaptive video streaming to improve its quality-of-experience (QoE) in the presence of network quality variations. Much of the existing research either employed dummy video contents or open source videos for QoE evaluation which may not capture the full spectrum of characteristics of real-world contents. This work fills this gap by developing a novel EmuStream platform based on real-world contents to enable realistic experiments and evaluation of any adaptive streaming algorithm in any streaming platform. First, EmuStream offers the largest (700+ titles) publicly available video bitrate trace dataset derived from real-world video contents. Second, we developed a mathematical model based on the dataset which can generate bitrate trace data for arbitrary target bitrates without actual video encoding. Third, we developed a novel Virtual Video Generator (VVG) which can generate h.264/h.265-compliant virtual videos that share the same frame/segment sizes as the source videos to enable experiments mimicking the streaming of commercial video contents. Last but not least, we developed a novel Streaming Performance Meter (SPM) that can measure detailed frame-by-frame playback performance from a video recording of the streaming playback of a virtual video. By decoding the virtual video's specially-coded frames, SPM can determine the playback timing and bitrate selected for each frame for use in computing any desired QoE metric. This paper presents the EmuStream platform and validates the trace data estimation model and VVG/SPM tools via controlled experiments.

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“…Moreover, in some implementations, such as MPEG-DASH [3], the HAS technology is independent of video compression methods and can utilize various codecs. This is especially important because we are now in a situation where we can choose from several video codecs, such as Advanced Video Coding (AVC) [5], High Efficiency Video Coding (HEVC) [6], VP9 [7], AOMedia Video 1 (AV1) [8], and Versatile Video Coding (VVC) [9]. Typically, the transcoding of video segments is a parallel process running on a high-performance infrastructure such as the cloud [10,11].…”

Section: Introductionmentioning

confidence: 99%

ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Sequences Using Artificial Neural Network

Zabrovskiy

Agrawal

Mathá

et al. 2020

2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)

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HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today's traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state of the art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.

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A Technical Overview of VP9—The Latest Open-Source Video Codec

Cited by 95 publications

References 3 publications

A Technical Overview of AV1

A Technical Overview of AV1

EmuStream—An End-to-End Platform for Streaming Video Performance Measurement

ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Sequences Using Artificial Neural Network

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