Adaptive High Efficiency Video Coding Based on Camera Activity Classification

We present a framework for adaptive video encoding based on video content. The basic idea is to analyze the video to determine camera activity (tracking, stationary, or zooming) and then associate each activity with adaptive video quality constraints. We demonstrate our approach on the UT LIVE video quality assessment database that effective camera activity detection and classification is possible based on the motion vectors and the number of prediction units (PU) extracted using x265 HEVC encoding standard. In our results, by applying leave-one-out validation, we get an 79% correct classification rate using kNN binary classifier for the video segments.