Interpolation Techniques; Simulation

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Interpolation Techniques Simulation Analysis
CS 584 Multimedia Communication

• Asmar Azar Khan
• 2005-06-0003

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Objective

• Study and analyze different interpolation
  techniques
• Performance analysis of these techniques on
  different performance metrics
• To propose a hardware based approach for
  Interpolation

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Agenda

• Introduction
• Transcoding
• Image Scaling
• Literature Review
• Interpolation Techniques
• Linear
• Nearest
• Cubic
• Spline
• Proposed Algorithm
• Filtering
• Design Implementation
• Memory Requirement
• Delay Requirement
• Future Proposals
• Questions

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Introduction

• Interoperability of multimedia devices
• Each device has different encoder and hence
  decoding schemes
• Broadband TV and Video on demand
• PDA and Mobile
• Online Gaming
• Internet Telephony
• Role of Transcoders and Image Scaling

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Transcoding

• A steer demand of multimedia on network have
  given rise to challenges
• Heterogonous Encoders and Decoders
• Bit Rate ( video)
• Delay ( voice and video)
• Quality ( multimedia)
• A technique where we change the encoded bit
  stream on the fly according to receiver
  compatibility
• Encoded scheme
• Error Correction Techniques
• Spatial and Temporal Resolution

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Image Scaling

• Spatial Resolution of Image and Video
• HDTV ( 1620 x 1200) etc
• PC Monitors ( 1280 x 800) XVGA
• PDA (640 x 480) VGA
• Mobile (176 x 144) QCIF

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Literature Review

• Software based approaches
• Interpolation Techniques
• Nearest Neighbor
• Linear
• Cubic Spline
• Bicubic Spline
• Hardware based Nearest Neighbor
• Simulations results for Advanced Techniques
• Recently DCT domain Interpolation has been
  presented

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Interpolation

• Whenever an image is desired to be re-sampled
• It is first interpolated to continuous image
• Then the image is sampled

Scaled Image
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Interpolation Methods

• Nearest Neighbor
• Linear
• Quadratic
• Cubic B Spline
• Normal

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Nearest Neighbor

• Nearest Pixel Value
• Less Complex
• Low Quality
• Edge handling

Courtesy Thomas M. Lehmann , Survey
Interpolation Methods in Medical Image Processing
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18,
NO. 11, NOVEMBER 1999
h(x)
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Linear Interpolation
Courtesy Thomas M. Lehmann , Survey
Interpolation Methods in Medical Image Processing
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18,
NO. 11, NOVEMBER 1999
1 - x ,
0 lt x lt 1
h(x)
0 ,
else
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Quadratic Interpolation
Courtesy Thomas M. Lehmann , Survey
Interpolation Methods in Medical Image Processing
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18,
NO. 11, NOVEMBER 1999
A1x2 B1x C1 , 0 lt x lt0.5
h(x)
A2x2 B2x C2 , 0.5 lt x lt1.5
0 , else
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Bicubic Interpolation
Courtesy Marco Aurelio Nuño-Maganda National
Institute for Astrophysics, Optics and
Electronics (INAOE)
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B-Spline Interpolation
Courtesy Thomas M. Lehmann , Survey
Interpolation Methods in Medical Image Processing
IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18,
NO. 11, NOVEMBER 1999
1/2x3 -x2 2/3 , 0lt x lt1
h(x)
-(1/6)x3 x2 -2x 4/3, 1lt x lt2
0 ,
else
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Simulation

• An image of 512 x 512 was taken
• It was reduced to 256 x 256
• Then interpolated using different techniques

512 x 512
512 x 512
Down sampling
Interpolation
256 x 256
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Preprocessing Low Pass Filter

• A 7-tap filter used for CCIR-601 to SIF
  conversion
• -29 0 88 138 88 0 -29 1/256

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Simulation Test Image
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SNR Calculation
Interpolation SNR (dbs)
Linear 13.0467
Nearest 12.9016
Bi-Cubic 13.0013
Spline 12.9886
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Histogram Analysis
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Nearest Neighbor
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Linear Interpolation
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Bicubic Interpolation
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Spline Interpolation
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Nearest Neighbor
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Linear Interpolation
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Bicubic Interpolation
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Spline Interpolation
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Fourier Transform of Original Image
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Fourier Transform of Nearest Neighbor
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Fourier Transform of Linear
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Fourier Transform of Bicubic
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Fourier Transform of Spline
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Re sampling Algorithm

• Low pass filter is applied to avoid aliasing
• Up sampling is done first in horizontal direction
  means column wise and then vertical direction
  i.e. row wise.
• 40/11 is non integer factor
• Up sample by 40
• Down sample by 11
• Similarly 10/3 factor

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Mapping onto scaled image
There will be 768 blocks/slices of original and
scaled image
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Post Processing

• A 7-tap filter used to convert SIF to CCIR-601
• -12 0 140 256 140 0 -12 1/256
• Removes the blocking effects from the
  interpolated image by introducing blurring
• As nearest neighbor techniques introduces sudden
  changes due to boundary value problems

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Controller based approach

• Distributed memory architecture
• State Machine based hardware
• Pre processing filtering
• Post processing filtering
• Memory Read and Write

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Distributed Memory Architecture
Mem in
Mem out
Mem out
Mem in
Input Image
Output Image
Mem out
Mem in
Mem in
Mem out
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11 to 40 Mapping
a b c d e f g h i j k
a a a b b b b c c c d d d d e e e e f f f g g g g h h h h i i i j j j j k k k k
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3 to 10 Mapping
a b c
a a a b b b b c c c
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System Block Diagram
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Future Proposals

• Advanced Interpolation methods
• Cubic Spline
• Normal Spline
• Generic Conversion
• Generic scaling ratio

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Questions !
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I thank you for your time..?