CONTENT ADAPTIVE UPDATE STEPS FOR LIFTING-BASED MOTION COMPENSATED TEMPORAL FILTERING

Abstract

A fundamental difference in the MCTF coding scheme from the conventional compensated DCT schemes is that the predicted residue is further used to update the temporal low-pass frames. However, it may cause the annoying ghost artifact if the predicted residues are generated by inaccurate motion prediction and several temporal highpass frames are dropped. This paper proposes a content adaptive update scheme, where the HVS (Human Vision System) model is used to evaluate the impact of the update steps in terms of visual quality at the low-pass frames. The potential ghost artifacts detected by the model can be alleviated by adaptively removing visible part of the predicted residues. Experimental results show that the proposed algorithm not only significantly improves subjective visual quality of the temporal low-pass frames but also maintains the PSNR performance compared with the normal full update.