High quality deinterlacing using content adaptive vertical temporal filtering

In this paper, we propose a content adaptive vertical temporal filtering method for deinterlacing which effectively uses correlations between adjacent frames. The proposed method consists of two steps: the initial interpolation step and the enhancement step. During the initial interpolation step, missing lines were reconstructed by applying a modified content adaptive vertical temporal filtering (MCAVTF) method. The MCAVTF method is proposed to improve the classification accuracy of the video content. After the initial interpolation step, the reconstructed lines were refined by using an adaptive weighted vertical temporal filtering (AWVTF) method. A field averaging filter was also used to enhance stationary local regions. Since we did not use motion estimation, the proposed method was of low complexity. Experimental results show that the proposed method outperforms existing methods in terms of subjective and objective evaluations.