Motion compensation using adaptive rectangular partitions
We present the partitioning of grey-scale images using rectangular partitions, and its use for image prediction by motion compensation. Generally, a good prediction of a video frame can be made if an accurate motion estimation is computed, most often by using block matching algorithms (BMA). We propose to use an adaptive rectangular partition in order to improve the motion estimation and compensation steps. Rectangular partitioning presents the advantage of being well adapted to the image texture and to generate variable size blocks. Taking into account these properties, we test different adaptations of the classical BMA to the rectangular partitioning, in order to improve the motion compensation performance. We consider the simple translational BMA and extend this algorithm by using spatial transformations in order to cope with rotations and scalings. The last algorithm is furthermore optimized by taking into account the shape of the rectangles in the partition. Simulations show that the last method provides higher PSNR than the two other.