  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.

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