Publication: Green video complexity analysis for eff...
Master data
Title: | Green video complexity analysis for efficient encoding in Adaptive Video Streaming |
Subtitle: | |
Abstract: | For adaptive streaming applications, low-complexity and accurate video complexity features are necessary to analyze the video content in real time, which ensures fast and compression-efficient video streaming without disruptions. State-of-the-art video complexity features are Spatial Information (SI) and Temporal Information (TI) features which do not correlate well with the encoding parameters in adaptive streaming applications. To this light, Video Complexity Analyzer (VCA) was introduced, determining the features based on Discrete Cosine Transform (DCT)-energy. This paper presents optimizations on VCA for faster and energy-efficient video complexity analysis. Experimental results show that VCA v2.0, using eight CPU threads, Single Instruction Multiple Data (SIMD), and low-pass DCT optimization, determines seven complexity features of Ultra High Definition 8-bit videos with better accuracy at a speed of up to 292.68 fps and an energy consumption of 97.06% lower than the reference SITI implementation. |
Keywords: | Video complexity analysis, Discrete cosin transform, Multi-threading, Low-pass optimization |
Publication type: | Article in Proceedings (Authorship) |
Publication date: | 07.06.2023 (Print) |
Published by: |
GMSys '23 Proceedings of the First International Workshop on Green Multimedia Systems
GMSys '23 Proceedings of the First International Workshop on Green Multimedia Systems
(
ACM Digital Library;
)
to publication |
Title of the series: | - |
Volume number: | - |
First publication: | Yes |
Version: | - |
Page: | pp. 16 - 18 |
Versionen
Keine Version vorhanden |
Publication date: | 07.06.2023 |
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ISSN: | - |
Homepage: | https://dl.acm.org/doi/10.1145/3593908.3593942 |
Publication date: | 09.06.2023 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1145/3593908.3593942 |
Homepage: | https://dl.acm.org/doi/10.1145/3593908.3593942 |
Open access |
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Authors
Vignesh Menon (internal) | ||||
Christian Feldmann
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Klaus Schöffmann (internal) | ||||
Mohammed Ghanbari (external) | ||||
Christian Timmerer (internal) |
Assignment
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Peer reviewed |
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bitmovin GmbH
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AT - 9020 Klagenfurt am Wörthersee |
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University of Essex
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GB - C04 3SQ Colchester |
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