Hybrid Object-Based Video Compression Scheme Using a Novel Content-Based Automatic Segmentation Algorithm

This paper describes a hybrid object-based video coding scheme that achieves efficient compression by separating moving objects from stationary background and transmitting the shape, motion and residuals for each segmented object. In this scheme, a new content-based object segmentation algorithm is proposed, which does not assume any prior modeling of the objects being segmented. The binarization process, which finds large object regions, is based on a threshold function that calculates block histograms and takes image noise into account. The resultant binary mask is further processed using morphological operations. The motion vectors are estimated inside the change detection mask using block-matching method between two successive frames, and then the dense motion field is estimated using the motion vectors and the Horn-Schunck algorithm.