Efficient adaptive deinterlacing algorithm with awareness of closeness and similarity

We introduces an efficient intra-field deinterlacing algorithm while considering both closeness and similarity between the interpolated pixel and the neighbor pixels. Instead of estimating the edge orientations via limited candidate directions as previous intra-field deinterlacing methods, we propose an adaptive spatial locality-and similarity-based deinterlacing method. Many deinterlacing methods described in the literature only consider the similarity between neighbor pixels and interpolated pixels; however, awareness of distance correlation and spatial locality has not yet been studied. Experimental results indicate that the proposed scheme outperforms a number of existing approaches, in terms of both the objective and subjective performance. 1 Introduction Interlaced scanning has been widely employed as the standard of TV broadcast systems, such as National Television Systems Committee (NTSC) and Sequential Color with Memory (SECAM), and some of the high definition television (HDTV) formats, because this method allows for not only the reduction in bandwidth and memory but also the maintenance of the existing transmission channel. However, this interlaced scanning format introduces several problematic artifacts, known as line crawling, edge flickering, interline flickering, field aliasing, and so on. 1–3 Moreover, as the screen size increases, the artifacts become more troublesome. Deinterlacing methods have been developed to minimize these issues. Deinterlacing methods can be roughly classified into two categories. One is the intra-field spatial domain method that only uses one field 3–14 and the other is an inter-field temporal deinterlacing method that uses multiple fields. Inter-filed temporal deinterlacing includes motion adaptive and motion compensation (MC) methods.