Edge Oriented Hierarchical Motion Estimation For Video Coding

Efficient video compression relies heavily on mitigating the temporal redundancy that exists between successive video frames. This is achieved through effective motion modelling. In conventional video coding standards, the motion of the current frame is modelled from the neighbouring frames using block-based motion estimation techniques. However, as the motion discontinuities are tied to the moving objects in a video frame, the block-based techniques are unable to model the actual motion of individual objects. In this paper, an object-based hierarchical motion estimation and prediction technique for high-efficiency video coding (HEVC) is proposed. We use an edge position difference (EPD) similarity measure, which has the ability to align the largest object in the frames, to estimate the motion of the object in the current frame from the neighbouring one. In other words, it estimates the largest object’s motion instead of the whole frame’s global motion. The proposed method gradually models all of the objects’ motions and establishes a prediction of the current frame. The predicted frame is then exploited as an additional reference frame in the HEVC compression algorithm. Our experimental results demonstrate that our proposed approach achieves a bit rate savings with a peak signal to noise ratio (PSNR) gain over the HEVC standard.