Improved picture-rate conversion using classification-based LMS-filters

Due to the recent explosion of multimedia formats and the need to convert between them, more attention is drawn to picture rate conversion. Moreover, growing demands on video motion portrayal without judder or blur requires improved format conversion. The simplest conversion repeats the latest picture until a more recent one becomes available. Advanced methods estimate the motion of moving objects to interpolate their correct position in additional images. Although motion blur and judder have been reduced using motion compensation, artifacts, especially around the moving objects in sequences with fast motion, may be disturbing. Previous work has reduced this so-called 'halo' artifact, but the overall result is still perceived as sub-optimal due to the complexity of the heuristics involved. In this paper, we aim at reducing the heuristics by designing LMS up conversion filters optimized for pre-defined local spatio-temporal image classes. Design and evaluation, and a benchmark with earlier techniques will be discussed. In general, the proposed approach gives better results

Quality adaptive trained filters for compression artifacts removal

A compression artifacts removal algorithm that is adaptive to the artifact visibility level of the input video signal is proposed. The artifact visibility is determined per frame by the ratio of the accumulated gradient on the block edges to that of the remaining area. The filtering of each video frame is optimized using a least mean square mechanism which trains on pairs of target images and decompressed images of similar quality as the input frame. Experimental results show that the proposed approach outperforms several recent methods in coding artifact reduction