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

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

  • Experimental Results

  • Conclusion


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)


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)


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


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:


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 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.


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 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 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 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 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)

  • 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)

  • 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)

  • 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

  • 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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