The University of British Columbia

In this paper, we present a video chrominance subsampling method using feedforward artificial neural networks (FANNs). Experimental results show that our method outperforms spatial subsampling obtained via low pass filtering and decimation both objectively and subjectively. Other advantages of our algorithm are computational efficiency and low memory requirements. Moreover, no pre- or post-processing is required by our method