The 13th ACM Multimedia Systems Conference (ACM MMSys 2022) Open Dataset and Software (ODS) track
June 14–17, 2022 | Athlone, Ireland
Vignesh V Menon, Christian Feldmann (Bitmovin, Klagenfurt), Hadi Amirpour, Mohammad Ghanbari, and Christian Timmerer
Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt
|VCA in content-adaptive encoding applications|
For online analysis of the video content complexity in live streaming applications, selecting low-complexity features is critical to ensure low-latency video streaming without disruptions. To this light, for each video (segment), two features, i.e., the average texture energy and the average gradient of the texture energy, are determined. A DCT-based energy function is introduced to determine the block-wise texture of each frame. The spatial and temporal features of the video (segment) are derived from the DCT-based energy function. The Video Complexity Analyzer (VCA) project aims to provide an efficient spatial and temporal complexity analysis of each video (segment) which can be used in various applications to find the optimal encoding decisions. VCA leverages some of the x86 Single Instruction Multiple Data (SIMD) optimizations for Intel CPUs and multi-threading optimizations to achieve increased performance. VCA is an open-source library published under the GNU GPLv3 license.
Online documentation: https://cd-athena.github.io/VCA/
Acknowledgments: The financial support of the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Christian Doppler Research Association, is gratefully acknowledged. Christian Doppler Laboratory ATHENA: https://athena.itec.aau.at/.
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