A motion adaptive deinterlacing method based on human visual system

Based on the analysis of conventional deinterlacing methods, a new motion adaptive deinterlacing method is proposed. In this paper, we improve three aspects of the conventional motion adaptive method. Firstly, we add a FIR filter to the motion detection part, which not only avoids the wrong detecting of moving objects but also reduces the noises effectively. Secondly, according to the fact, that human eyes are less sensitive to lighter or darker area than gray area, a threshold adaptive method is introduced, which enhanced the subjective quality of deinterlacing. Thirdly, we adopted different interpolation methods for different pixels, which improved the deinterlacing quality of motionless videos. The simulation results show that our proposed deinterlacing method can achieve higher PSNR (peak signal noise ratio) than that of previous studies and can also attain better quality of subjective view.