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.

翻译：在自适应流媒体应用中，低复杂度且准确的视频复杂度特征对于实时分析视频内容至关重要，这能确保流畅且压缩高效的无中断视频流传输。现有最先进的视频复杂度特征——空间信息（SI）和时间信息（TI）特征，在自适应流媒体应用中与编码参数的关联性不佳。为此，我们引入了基于离散余弦变换（DCT）能量特征确定的视频复杂度分析器（VCA）。本文提出了针对VCA的优化方案，以实现更快且更节能的视频复杂度分析。实验结果表明，采用八CPU线程、单指令多数据流（SIMD）及低通DCT优化的VCA v2.0版本，在分析超高清8位视频的七个复杂度特征时，准确率更高，处理速度可达292.68帧/秒，且能耗比参考SITI实现降低97.06%。