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Deinterlacing using Second Order Polynomial Estimation

机译:使用二阶多项式估计去隔行

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Deinterlacing has been an essential technology for modern video broadcasting services. A number of deinterlacing algorithms have been proposed such as intra or inter-field deinterlacing algorithms. Furthermore, deinterlacing algorithms that use neural networks also have shown promising performance. As broadcasting services with high resolution such as HD and UHD are becoming widely available, processing time and hardware complexity issues have become important. In this paper, we propose second order polynomial methods which can be substituted for neural network algorithms. We also applied the polynomial algorithm to deinterlacing problems. Experimental results showed that deinterlacing using second order polynomials was more efficient than using neural network algorithms in terms of PSNR and processing time.
机译:Deinterlacing是现代视频广播服务的必备技术。 已经提出了许多去隔行算法,例如帧内或场间去隔行算法。 此外,使用神经网络的去隔行算法也表现出了有希望的性能。 由于具有高分辨率(如HD和UHD)的广播服务正被广泛可用,处理时间和硬件复杂性问题变得重要。 在本文中,我们提出了可以代替神经网络算法的二阶多项式方法。 我们还将多项式算法应用于去隔行问题。 实验结果表明,在PSNR和处理时间方面,使用二阶多项式的去隔行比使用神经网络算法更有效。

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