CLASSIFIED PATCH LEARNING FOR SPATIALLY SCALABLE VIDEO CODING

This paper proposes an advanced spatially scalable video coding approach that exploits the inter layer correlation between different resolution layers by classified patch learning. The novelty of our proposed scheme is twofold. First, the correlation between low and high resolution frames is explored at patch level with regard to image features. Patches extracted from the previous coded frame are classified into structural and textural sets according to the gradient information. Then the inter layer correlation is separately studied for the two sets, resulting in two databases containing pairs of patches at different resolutions. Second, our proposed patch-based compensation manages to simultaneously exploit the spatial and temporal redundancies without overhead bit for motion. Based on the two databases, a high resolution prediction is derived from the current low resolution reconstruction at structural and textural regions, respectively. Experimental results show that our proposed approach improves the performance of H.264/MPEG spatially scalable coding up to 1.9dB and significantly enhances the subjective quality, especially at low bit rates.