Multi-Codec DASH Dataset
Anatoliy Zabrovskiy, Christian Feldmann, Christian Timmerer

TL;DR
This paper introduces a multi-codec DASH dataset with AVC, HEVC, VP9, and AV1 to facilitate interoperability testing, streaming experiments, and evaluation of encoding efficiency across different codecs.
Contribution
It provides a comprehensive multi-codec DASH dataset with encoding options, quality metrics, and a preliminary evaluation of encoding efficiency for adaptive streaming.
Findings
The dataset enables interoperability testing across multiple codecs.
Preliminary results show differences in encoding efficiency among codecs.
The dataset supports future research in adaptive streaming and codec performance.
Abstract
The number of bandwidth-hungry applications and services is constantly growing. HTTP adaptive streaming of audio-visual content accounts for the majority of today's internet traffic. Although the internet bandwidth increases also constantly, audio-visual compression technology is inevitable and we are currently facing the challenge to be confronted with multiple video codecs. This paper proposes a multi-codec DASH dataset comprising AVC, HEVC, VP9, and AV1 in order to enable interoperability testing and streaming experiments for the efficient usage of these codecs under various conditions. We adopt state of the art encoding and packaging options and also provide basic quality metrics along with the DASH segments. Additionally, we briefly introduce a multi-codec DASH scheme and possible usage scenarios. Finally, we provide a preliminary evaluation of the encoding efficiency in the…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · AI in cancer detection · Gene expression and cancer classification
