A Region Adaptive Motion Estimation Strategy Leveraging on the Edge Position Difference Measure
Ashek Ahmmed, Manoranjan Paul, Manzur Murshed, Andrew J. Lambert, Mark R. Pickering · 2022
To capture motion homogeneity between successive frames, the edge position difference (EPD) measure based motion modeling (EPD-MM) has shown good motion compensation capabilities. The EPD-MM technique is underpinned by the fact that from one frame to next, edges map to edges and such mapping can be captured by an appropriate motion model. However, the EPD-MM approach may produce inferior quality motion model in those regions of the current frame where moving edges are few in number. For such regions, traditional pixel intensity difference (PID) measure based motion modeling (PID-MM) may yield superior motion compensation. Therefore, in this paper, the entire current frame is at first partitioned into two regions (edge dominant region and edge sparse region) based on the frequency of moving edge pixels. This segmentation is carried out over the EPD image since it possesses information pertinent to the distance of every pixel from its nearest edge. After that for motion modeling, in the edge dominant region, the EPD-MM technique is adopted and for the rest of the current frame regions, the PID-MM approach is chosen. Experimental results show an improved prediction PSNR of 1.90 dB from the proposed approach compared to that of the baseline EPD-MM approach that does not differentiate between edge dominant and edge sparse regions. Moreover, if this predicted frame is employed as an additional reference frame to encode current frames, bit rate savings of up to 7.84% is achievable over a HEVC reference codec.