GMSys 2023: First International ACM Green Multimedia Systems Workshop
7 – 10 June 2023 | Vancouver, Canada
Vignesh V Menon (Alpen-Adria-Universität Klagenfurt), Christian Feldmann (Bitmovin, Klagenfurt), Klaus Schoeffmann (Alpen-Adria-Universität Klagenfurt), Mohammed Ghanbari (University of Essex), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt).
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. The popular 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 VCAv2.0, 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 292.68 fps and an energy consumption of 97.06% lower than the reference SITI implementation.
Content-adaptive encoding framework using video content complexity analysis.
