Moving Least-Squares Method for Interlaced to Progressive Scanning Format Conversion

摘要

In this paper, we introduce an efficient intra-field deinterlacing algorithm based on moving least squares (MLS). The MLS algorithm has proven successful for approximating scattered data by minimizing a weighted mean-square error norm. In order to estimate the value of the missing point using the given data, we utilize MLS to generate a generic local approximation function about this point. In the MLS method, we adopt trigonometric functions to approximate the local function. This method is compared to other benchmark algorithms in terms of peak signal-to-noise ratio and structural similarity objective quality measures and deinterlacing speed. It was found to provide excellent performance and the best quality-speed tradeoff among the methods studied.