Affine Motion-compensated Prediction - PowerPoint PPT Presentation

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Affine Motion-compensated Prediction

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Affine additional 4 parameters. Account for rotation and shear ... Hybrid affine tracker. Use blockmatching to determine several minima of the SSD function ... – PowerPoint PPT presentation

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Title: Affine Motion-compensated Prediction


1
Affine Motion-compensated Prediction
  • Dragomir Anguelov
  • EE368B Final Project
  • December 8, 2000

2
Motivation
  • Motion models used for prediction
  • Translation
  • How about more complex motions rotation, shear?
  • Tradeoff increased expressivity vs. increased
    number of parameters

3
Motion Models
  • Translation 2 parameters
  • Affine additional 4 parameters
  • Account for rotation and shear

4
The Motion-compensated Hybrid Coder
  • Testbed assumptions
  • Want very good image reconstruction
  • Details
  • Subpixel accuracy
  • No filtering
  • DCT Entropy-Minimizing coder with uniform
    quantization
  • Error image encoded and transmitted

5
The Hybrid MCHC
6
Affine Motion Estimation Overview
  • Blockmatching techniques
  • Infeasible, search space in 6 dimensions,
    optimizations in this space not well studied
  • Differential techniques
  • Lucas-Kanade pyramidal tracker (used in this
    project) Lucas, Kanade 81 Shi, Tomasi, 94
  • Mixture techniques

7
Lucas-Kanade tracker
  • Minimizes
  • Newton-Raphson minimization using the derivatives
    of the error function
  • Issues
  • Assumes motions between frames are not too large
  • Hierarchical implementation

8
Hybrid affine tracker
  • Use blockmatching to determine several minima of
    the SSD function
  • Initialize a differential method with those
    points and pick the best resulting set of affine
    parameters

9
Experimental results (1)
  • Setup
  • Padded image borders
  • Affine parameter quantization with step 0.2
  • DCT error quantization with step 1.0
  • Block size 16

10
Experimental Results(2)
  • Small, translation motion between frames
  • Hybrid MCHC rates are comparable

Frame 1 2 3 4 Trate 4.1359 2.4845 2.4557
2.5346 Hrate 4.1359 2.4846 2.4556 2.5350 Diff
0 -0.0001 0.0001 -0.0004
11
Experimental Results (3)
  • Relatively large motion, rotation present

Frame 1 2 3 4 TRate
5.6845 3.8926 3.8737
3.8393 Hrate 5.6845 3.8927 3.8702
3.8382 RateDiff 0 -0.0001
0.0035 0.0011
12
Experimental Results (4)
  • Block Choices
  • 0 translation 1 affine

13
Experimental Results (5)
  • Block size 32, param quantization step 0.1
  • Frame 1 2 3 4
  • Trate 5.8776 4.0643 4.0980
    4.0747
  • Hrate 5.8776 4.0608 4.0851
    4.0704
  • Diff 0 0.0035 0.0131 0.0043

14
Conclusions
  • Not too effective system
  • Small improvements
  • Increases running time
  • Limitation on accuracy of transmission and amount
    of motion
  • Improvement (better rate, same quality)
  • Larger blocksizes
  • Complex motions
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