Motion Adaptive Deinterlacing via Edge Pattern Recognition

Abstract

In this paper, a novel edge pattern recognition (EPR) deinterlacing algorithm with successive 4-field enhanced motion detection is introduced. The EPR algorithm surpasses the performance of ELA-based and other conventional methods especially at textural scenes. In addition, the current 4-field enhanced motion detection scheme overcomes conventional motion missing artifacts by gaining good motion detection accuracies and suppression of "motion missing" detection errors efficiently. Furthermore, with the incorporation of our new successive 4-field enhanced motion detection, the interpolation technique of EPR algorithm is capable of flexible adaptation in achieving better performance on textural scenes in generic video sequences.

Algorithm/Architecture Co-Exploration of Visual Computing on Emergent Platforms: Overview and Future Prospects

“…Similarly, the complexity of video processing algorithms, such as motion adaptive deinterlacers [1], [2], scan-rate converters, and other format converters, is also characterized by studying the three complexity features discussed above. Content-adaptive algorithms for high definition video processing, such as those in scalers, deinterlacers, etc., which are based on the texture contents from surrounding neighbors, have also been documented in the literature [3], [4] and used in many applications.…”

An efficient motion adaptive de-interlacing algorithm using spatial and temporal filter

“…Non-MC methods include spatial, temporal, and motion adaptive schemes. Some well known spatial algorithms are line repetition, vertical line averaging edge based interpolation (EDI) [3]- [5]. Spatial methods use only one field to exploit the correlation between the spatial neighboring pixels for interpolating missing lines.…”