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Pixel Interpolation

By: Mieng Phu

Supervisor: Peter Tischer

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

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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.

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Before After

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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

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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.

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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

SymbolEncoder

Compressed Image

Predictor

Input Image

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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.

100 100 100 100 10050 50 50 50 50100 100 ?

100 100 100 100 100100 100 100 100 100100 100 ?

known values

How would we predict this ?

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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

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Interlacing Video and Deinterlacing

• A complete frame

Odd line

Even line Lower or even field

Upper or odd field

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• 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

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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)

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Deinterlacing(1)

• Deinterlacing within a single frame - use the odd lines to predict the even lines.

x x x

x x x

? ? ?

x - Known values

? - Unknown values

Current field

Time ti

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Deinterlacing(2)

• Deinterlacing of two frames - use the even lines of the previous frame and odd lines of the current frame, also motion vectors.

? ? ?

? ? ?

x x x

x x x

x x x

? ? ?

Current fieldPrevious field

ti - 1 ti

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Deinterlacing(3)• Motion Compensation and Estimation- use previous,

current and future frame with motion vectors to create a highly quality and resolution video.

? ? ?

? ? ?

x x x

x x x

x x x

? ? ?

? ? ?

? ? ?

x x x

ti - 1 ti ti +1

Previous field Current field Future field

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

x ? x

x ? x

? ? ?

HDTV

x x

x x

SDTV

Magnification

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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

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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.

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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.

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Any Questions..