High-Quality Spatial Interpolation of Interlaced Video Alexey Lukin Moscow State University, 2008.

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Presentation transcript:

High-Quality Spatial Interpolation of Interlaced Video Alexey Lukin Moscow State University, 2008

2/13 Interlaced-scan video Invented in 1930-ies Invented in 1930-ies Video frame is separated into 2 fields (even and odd raster lines) Video frame is separated into 2 fields (even and odd raster lines) Improvement of motion smoothness w/o increase of signal bandwidth Improvement of motion smoothness w/o increase of signal bandwidth

3/13 Interlaced-scan video Invented in 1930-ies Invented in 1930-ies Video frame is separated into 2 fields (even and odd raster lines) Video frame is separated into 2 fields (even and odd raster lines) Improvement of motion smoothness w/o increase of signal bandwidth Improvement of motion smoothness w/o increase of signal bandwidth

4/13 Deinterlacing Computer displays are progressive-scan → deinterlacing is needed Computer displays are progressive-scan → deinterlacing is needed Simplest deinterlacing methods: Simplest deinterlacing methods: ► “Bob” (line averaging) ► “Weave” (field insertion) spatial interpolation temporal interpolation

5/13 Deinterlacing Advanced deinterlacing methods: Advanced deinterlacing methods: ► Motion-adaptive: use “Bob” method in motion areas, use “Weave” method in still areas ► Motion-compensated: use motion compensation to temporally align fields in motion-adaptive method

6/13 Spatial interpolation High-quality spatial interpolation High-quality spatial interpolation Simple approaches: Simple approaches: ► Line averaging, cubic interpolation ► ELA (Edge-Directed Line Averaging)

7/13 Spatial interpolation Problem with ELA: uncertain interpolation direction in presence of thin lines Problem with ELA: uncertain interpolation direction in presence of thin lines Line averagingELA (5 directions)

8/13 Proposed method Aperture extension Aperture extension ► Allows interpolating near-horizontal edges Spatial averaging of derivatives Spatial averaging of derivatives ► Improves the robustness of edge sensing

9/13 Proposed method Mixing of interpolation directions Mixing of interpolation directions ► Tolerates inaccurate detection of edge direction Expectation Maximization Expectation Maximizationalgorithm ► Re-estimation of derivatives from the interpolated image

10/13 Results PSNR evaluation

11/13 Results Visual quality Line averagingELA (5-directional) EEDI2 method (free software) EDDI method (G. de Haan)MSU algorithm, 2003Proposed method

12/13 Results Visual quality ELA (5-directional)Proposed method + motion compensation

13/13 Conclusion The developed method has: The developed method has: ► High visual quality and good PSNR figures ► Simple structure ► High computational complexity (optimizations possible)

14/13 Your questions ? Thank you