Low bit-rate coding of image sequences using adaptive regions of interest

An adaptive algorithm for extracting foreground objects from background in videophone or videoconference applications is presented in this paper. The algorithm uses a neural network architecture that classifies the video frames in regionsof-interest (ROI) and non-ROI areas, also being able to automatically adapt its performance to scene changes. The algorithm is incorporated in motion-compensated discrete cosine transform (MC–DCT)-based coding schemes, allocating more bits to ROI than to non-ROI areas. Simulation results are presented, using the Claire and Trevor sequences, which show reconstructed images of better quality, as well as signal-to-noise ratio improvements of about 1.4 dB, compared to those achieved by standard MC–DCT encoders.