Object based coding of video sequences at low bit rates using adaptively trained neural networks
Nikolaos D. Doulamis, Anastasios D. Doulamis, Stefanos Kollias · 2003
Unsupervised video object segmentation is proposed in this paper, using an adaptively trained neural network structure followed by a face and body detection scheme. The latter uses probabilistic modeling for applying the face and body detection task. The algorithm is incorporated along with a rate control mechanism, which allocates more bits to regions of importance, such as humans in video conferencing applications than to unimportant ones. The compatibility to block-based encoders, such as the MPEG-1/2 and the H.263, is retained, so that the proposed scheme can further improve their coding efficiency.