FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning

Çetinkaya, Ekrem; Amirpour, Hadi; Timmerer, Christian; Ghanbari, Mohammad

doi:10.1109/vcip49819.2020.9301850

Cited by 6 publications

(4 citation statements)

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“…CNNs are very effective at image processing and computer vision [198,199]. CNNs have also proven useful for video streaming services [200]. The studies in [200,201] presented some directions in which DL could be applied to improve the quality of video delivery.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…CNNs have also proven useful for video streaming services [200]. The studies in [200,201] presented some directions in which DL could be applied to improve the quality of video delivery. The authors of [200] proposed the use of a CNN to enable parallel encoding of video for HAS.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…The studies in [200,201] presented some directions in which DL could be applied to improve the quality of video delivery. The authors of [200] proposed the use of a CNN to enable parallel encoding of video for HAS. In parallel encoding, frames of a compressed video serve as a reference to define future frames.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

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A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

2021

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A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Discussion and Future Directionsmentioning

confidence: 99%

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A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

2021

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“…Çetinkaya et al [54] proposes a fast multi-rate encoding method using machine learning (FaMe-ML) with a specific focus on parallel encoding. The lowest quality representation is chosen as the reference.…”

Section: Multiratementioning

confidence: 99%

CTU depth decision algorithms for HEVC: A survey

Çetinkaya

Amirpour

Ghanbari

et al. 2021

Signal Processing: Image Communication

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High-Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1 (AV1).

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