A Block-Wise Autoregression-Based Deinterlacing Algorithm

Abstract

This paper introduces an autoregression-based single-field deinterlacing algorithm. The proposed algorithm provides a block-wise autoregression mechanism that considers mutual influence between the missing high-resolution pixels and the given interlaced, low-resolution pixels in a slip window. This method is compared with other benchmark methods in terms of PSNR, implementing speed, and subjective performance. It is found that, with the proposed algorithm, common artifacts such as serration, line crawl, and flicker are significantly reduced.