Motion-Compensated Frame Prediction with Global Motion Estimation for Image Sequence Compression

Based on a perspective projection model of six-parameter camera motions, a novel motion-compensated frame prediction approach is presented, taking into consideration all types of camera motions including camera translations. With the assumption of a continuously changing scene-depth distribution, a block-based scaled-depth estimation technique is proposed for obtaining the scaled-depth map of the predicted frame. The motion-compensated predicting frame is generated by using the global camera rotation and translation parameters combined with the scaled block-depth map. Compared with traditional block matching algorithms (BMA), more accurate predicting frames are obtained with the proposed motion-compensated frame prediction approach. As a result of fewer parameters required for motion compensation and reduced prediction residues, this approach is potentially more efficient if applied in frame prediction for image sequence compression. Experimental results on test images demonstrate the effectiveness of the proposed approach, and also demonstrate its superior performance over the traditional BMA applied for motion-compensated frame prediction.