A proposed video complexity measurement method to be used in a cluster computing

Cluster computing is widely used for image and video processing in entertainment applications. The video complexity can be experimentally determined according to the time spent to render it in a cluster. This paper analyzes the time spent for rendering 3D videos with different characteristics by a process of ray tracing on a cluster computing with Povray software. For another hand, the video complexity is determined by a novel metric named Video Complexity Index (VCI) that considers both, the spatial and temporal video characteristics. Experimental results demonstrate that VCI metric successfully classifies the videos regarding their complexity, considering the processing time consumed for a cluster. As a consequence, VCI metric can be useful to choose the right number of cluster nodes in accordance to the 3D video complexity.