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Pixel Interpolation

Pixel Interpolation. By: Mieng Phu Supervisor: Peter Tischer. Outline . What is pixel interpolation? Applications Project Aims Lossless Image Processing Image and Video Processing Methodology Work so far achieved Summary. What is pixel interpolation?.

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Pixel Interpolation

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  1. Pixel Interpolation By: Mieng Phu Supervisor: Peter Tischer

  2. Outline • What is pixel interpolation? • Applications • Project Aims • Lossless Image Processing • Image and Video Processing • Methodology • Work so far achieved • Summary

  3. What is pixel interpolation? • Pixel (or pels) is used to denote the elements of a digital image. An image is a 2D array of pixels with different intensity. • Interpolation is to alter, invent or introduce by insertion a new matter. • Hence, the fundamental concept of Pixel Interpolation to invent or predict missing pixels.

  4. Before After

  5. Applications • Image and Video Processing • Digital Camera-Color interpolation Scheme (CCD image sensor) • Printers • Internet - Web Browsers • Flat Panel Display (FPD) like LCD, Plasma.. • Medical science imaging. • Videophone

  6. Project Aims • The idea of this project is to look at how missing pixel values are estimated in lossless image processing (L.I.C). • Then to investigate how these techniques can be applied in other areas of image and video processing, where pixel interpolation is needed.

  7. Input Image Compressed Image  Symbol Encoder Predictor Lossless Image Compression (L.I.C) • The fundamental concept of L.I.C. reduce the amount of data required to represent an image, so that we can retain its originality. • Also known as Lossless Predictive Coding

  8. known values How would we predict this ? So how are missing pixel values estimated in L.I.C ? • Images are normally coded in raster order. • Based on the past input pixels, the predictor generates the anticipated value dependent on the predictor. • Various local, global, and adaptive predictors.

  9. Lossless Image Compression Techniques • Some lossless image compression prediction techniques are: • Local approximation • Polynomial exaction • exact for flat region • exact for linear gradient • Multiple Predictors • Switching • Blending • Least squares approaches

  10. Interlacing Video and Deinterlacing • A complete frame Upper or odd field Odd line Lower or even field Even line

  11. E.g. AB frame - odd lines from picture A and even lines from picture B with a time shift of 1/24 seconds - Object moving between fields. Position in field A Position in field B

  12. Image and Video Processing • In image and video processing, missing pixels must be estimated to avoid problems. • Situations where pixel interpolation is needed: • Deinterlacing within a single field • Deinterlacing using current and past field • Deinterlacing using the past, current and future field (motion compensation estimation) • SDTV to HDTV (Magnification)

  13. x x x ? ? ? x x x Deinterlacing(1) • Deinterlacing within a single frame - use the odd lines to predict the even lines. x - Known values ? - Unknown values Time ti Current field

  14. x ? ? x x ? x ? x ? ? x ? x x ? ? x Deinterlacing(2) • Deinterlacing of two frames - use the even lines of the previous frame and odd lines of the current frame, also motion vectors. ti - 1 ti Previous field Current field

  15. ? ? x ? ? x x ? ? ? x x x x ? x x ? ? ? x x ? ? ? x ? Deinterlacing(3) • Motion Compensation and Estimation- use previous, current and future frame with motion vectors to create a highly quality and resolution video. ti - 1 ti +1 ti Previous field Current field Future field

  16. x x ? x x ? x x ? ? x ? x Magnification • Converting from SDTV to HDTV - could be done by deinterlacing the rows and then deinterlacing the columns. SDTV HDTV

  17. Methodology • Start Points • Study still images and single frame • Try using known pixels from different positions. • Switching predictors from L.I.C • Blending predictors from L.I.C

  18. Work so far achieved ? • Implementation of Tao Chen Edge Line Averaging (ELA) algorithm for deinterlacing within a single frame. • Implementation of the existing algorithms for deinterlacing- generic ELA, Adaptive ELA, Line Doubling. • Comparison between algorithms. • Remarks: Tao Chen algorithm can be improved.

  19. Summary • There are many application on image and video processing in which missing pixel values must be estimated. • This project investigates how existing techniques from lossless image compression can be applied in other areas of image and video processing, where pixel interpolation needed.

  20. Any Questions..

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