Method switching algorithm for intra-field de-interlacing

In this paper, we propose an adaptive de-interlacing algorithm that is based on the degree of local complexity. Conventional intra-field de-interlacing methods which achieve good performance in terms of image quality have also higher computational burden; in addition, they show the different performance according to the ways of finding the edge direction. To develop a low-complexity de-interlacing method suitable for real-time application, the proposed algorithm adaptively adopts a de-interlacing algorithm among line average (LA), modified edge-based line average (MELA), and low-complexity and interpolation method for de-interlacing (LCID) algorithms, which have low complexity and good performance, and the proposed algorithm is trained using the degree of local complexity (DoLC) in order to determine an efficient algorithm among the algorithms mentioned above. Simulation results demonstrate that the proposed algorithm not only has the low complexity but also provides better objective and subjective image quality performance compared with the conventional intra-field methods.