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

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.

[1]  Joost van de Weijer,et al.  Least Squares and Robust Estimation of Local Image Structure , 2003, Scale-Space.

[2]  Erwin B. Bellers,et al.  De-Interlacing: A Key Technology for Scan Rate Conversion , 2000 .

[3]  Gerard de Haan,et al.  An Overview and Performance Evaluation of Classification-Based Least Squares Trained Filters , 2008, IEEE Transactions on Image Processing.

[4]  Moon Gi Kang,et al.  New edge dependent deinterlacing algorithm based on horizontal edge pattern , 2003, IEEE Trans. Consumer Electron..

[5]  Holger Wendland,et al.  Scattered Data Approximation: Conditionally positive definite functions , 2004 .

[6]  Ernesto Damiani,et al.  Concept of Linguistic Variable-Based Fuzzy Ensemble Approach: Application to Interlaced HDTV Sequences , 2009, IEEE Transactions on Fuzzy Systems.

[7]  Keith Jack,et al.  Video Demystified: A Handbook for the Digital Engineer , 1993 .

[8]  Donghyung Kim,et al.  Fuzzy rough sets hybrid scheme for motion and scene complexity adaptive deinterlacing , 2009, Image Vis. Comput..

[9]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[10]  Pei-Yin Chen,et al.  A Low-Complexity Interpolation Method for Deinterlacing , 2007, IEICE Trans. Inf. Syst..

[11]  Zheng Liu,et al.  Learning-based super resolution using kernel partial least squares , 2011, Image Vis. Comput..

[12]  Hong Ren Wu,et al.  Efficient deinterlacing algorithm using edge-based line average interpolation , 2000 .

[13]  Gwanggil Jeon,et al.  Weighted Fuzzy Reasoning Scheme for Interlaced to Progressive Conversion , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Gerard de Haan,et al.  Quality adaptive least squares trained filters for video compression artifacts removal using a no-reference block visibility metric , 2011, J. Vis. Commun. Image Represent..

[15]  Ernesto Damiani,et al.  Designing of a type-2 fuzzy logic filter for improving edge-preserving restoration of interlaced-to-progressive conversion , 2009, Inf. Sci..

[16]  Jechang Jeong,et al.  Novel Intra Deinterlacing Algorithm Using Content Adaptive Interpolation , 2007, IEEE Transactions on Consumer Electronics.

[17]  Gwanggil Jeon,et al.  Adaptive autoregressive deinterlacing method , 2011 .

[18]  G. de Haan.  Television Display Processing: Past & Future , 2007 .