Fast lossless multi resolution motion estimation for scalable wavelet video coding
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Fast Lossless Multi-Resolution Motion Estimation for Scalable Wavelet Video Coding. Yu Liu and King Ngi Ngan Department of Electronic Engineering The Chinese University of Hong Kong ISCAS2006, May 21-24, Island of Kos, Greece. Outline. Introduction Background Proposed Algorithm

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Fast Lossless Multi-Resolution Motion Estimation for Scalable Wavelet Video Coding

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Fast lossless multi resolution motion estimation for scalable wavelet video coding

Fast Lossless Multi-Resolution Motion Estimation for Scalable Wavelet Video Coding

Yu Liu and King Ngi Ngan

Department of Electronic Engineering

The Chinese University of Hong Kong

ISCAS2006, May 21-24, Island of Kos, Greece


Outline

Outline

  • Introduction

  • Background

  • Proposed Algorithm

  • Experimental Results

  • Conclusion


Introduction

Introduction

  • Motion Estimation in Critically-Sampled Wavelet Domain

    • Pro: basically free form the blocking effects

    • Con: inefficient in high bands

  • Motion Estimation in Shift-Invariant Wavelet Domain

    • Pro: perform ME more precisely and efficiently

    • Con: computational complexity

    • e.g. low-band-shift (LBS) and complete-to-overcomplete DWT (CODWT)


Background motion estimation in shift invariant wavelet domain 1

BackgroundMotion Estimation in Shift-Invariant Wavelet Domain (1)

  • Two Level Shift-Invariant Wavelet Decomposition by using Low-Band-Shift (LBS) or Complete-to-Overcomplete DWT (CODWT)


Background motion estimation in shift invariant wavelet domain 2

Generation of Wavelet Blocks

BackgroundMotion Estimation in Shift-Invariant Wavelet Domain (2)

  • The v-pixel-shifted or {dx,dy}-pixel-shifted coefficient of the pth wavelet block of reference frame t’can be represented by

  • The coefficient of the pth wavelet block of current frame t can be represented by


Background motion estimation in shift invariant wavelet domain 3

BackgroundMotion Estimation in Shift-Invariant Wavelet Domain (3)

  • The sum of absolute difference (SAD) of the pth wavelet block for the motion vector v is computed as follows:

  • The optimum motion vector v∗ of the pth wavelet block, which has minimum displacement error, is given by:


Background partial distortion elimination

BackgroundPartial Distortion Elimination

  • Partial Distortion Elimination (PDE) is a fast algorithm which has identical quality as that of FSA.

  • The partial SAD (PSAD) is used to eliminate impossible candidates before the complete calculation of the SAD:


Proposed algorithm wavelet matching error characteristic

Proposed AlgorithmWavelet Matching Error Characteristic

  • To improve the computational saving of PDE, if the expected values of the matching error dp+v(i, j) in the search window w fulfills the following criterion:

  • then we assume that this matching order will be generally effective for all of the candidate vectors in the search window.

  • Therefore, to fulfill the above objective, we use ep(i, j) to predict the matching error.


Proposed algorithm wavelet matching error characteristic1

To obtain the predicted matching error ep(i, j), solve this equation

Proposed AlgorithmWavelet Matching Error Characteristic

  • We finally can obtain an approximate solution of Eq. (6):

  • Larger wavelet coefficientmagnitude in the current wavelet block tends to produce larger matching error


Proposed algorithm mr wmec pde

Proposed AlgorithmMR-WMEC-PDE

  • Three key factors which affect the performance of PDE

    • the Searching Order

      • in which the wavelet blocks are tested during the searching phase.

    • the Matching Order

      • in which the coefficients within a wavelet block are picked up to compute the SAD.

    • the Comparison Interval

      • in which comparison between PSAD and SADmin is performed.

  • Three new strategies for PDE by using wavelet matching error characteristic (WMEC) are proposed.


Proposed algorithm mr wmec pde1

Proposed AlgorithmMR-WMEC-PDE

  • Searching Order Strategy based on Wavelet Multi-Resolution Property

    • Instead of the spiral search order, the proposed searching order strategy uses the normalized partial SAD in LL subband level as the estimated SAD (ESAD)

  • Then, sort the ESAD using the counting sort algorithm inascending order to obtain the searching order SO = {vn|n = 0, ...,w−1}.


Proposed algorithm mr wmec pde2

Proposed AlgorithmMR-WMEC-PDE

  • Matching Order Strategy based on Wavelet-tree Grouping Scheme

    • A wavelet-tree grouping scheme according to spatial self-similarity property and matching error clustering property of wavelet coefficient

  • Sort Ep(Bl,bl,m) using the quick sort algorithm in descending order to obtain the matching order of level l: MOl = {bl,m | m = 0, ...,M − 1}


Proposed algorithm mr wmec pde3

Proposed AlgorithmMR-WMEC-PDE

  • Comparison Interval Strategy based on Adaptive Sub-blocks Checking Unit

    • In conventional PDE methods, fixed comparison interval, such as eight-pixels or sixteen-pixels checking unit, is usually adopted.

    • Combined with the wavelet-tree grouping scheme, an adaptive comparison interval strategy is proposed.

    • In the proposed strategy, every 2l−1 sub-blocks in the decomposition level l are used as the checking unit.


Experimental results 1

Experimental Results (1)

  • Simulation results are reported in the following ways:

    • operation number : used to compute the partial distortion

    • speed-up ratio : for motion estimation including the required overheads for comparison.

  • For performance comparison with other algorithms

    • Full Search Algorithm (FSA)

    • Spiral-PDE [5]

    • CPME-PDS [6]

    • proposed MR-WMEC-PDE


Experimental results 2

Experimental Results (2)

  • Average Operation Number per Block for Tested Algorithms

On average speed-up ratio in terms of operation number, MR-WMEC-PDE is better than Spiral-PDE and CPME-PDS by about 92% and 34%, respectively.


Experimental results 3

Experimental Results (3)

  • Average Execution Time per Frame for Tested Algorithms

On average speed-up ratio in terms of execution time, MR-WMEC-PDE is better than Spiral-PDE and CPME-PDS by about 84% and 63%, respectively.


Conclusion

Conclusion

  • Fast Lossless Multi-Resolution Motion Estimation Algorithm

    • Wavelet Matching Error Characteristic (WMEC)

    • Three New Strategies for PDE

      • Searching Order Strategy based on Wavelet Multi-Resolution Property

      • Matching Order Strategy based on Wavelet-tree Grouping Scheme

      • Comparison Interval Strategy based on Adaptive Subblocks Checking Unit


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