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Yu Liu and King Ngi Ngan Department of Electronic Engineering, The Chinese University of Hong Kong

Anisotropic Double Cross Search Algorithm using Multiresolution-Spatio-Temporal Context for Fast Lossy In-Band Motion Estimation. Yu Liu and King Ngi Ngan Department of Electronic Engineering, The Chinese University of Hong Kong PCS2006, April 24-26, Beijing, China. Outline. Introduction

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Yu Liu and King Ngi Ngan Department of Electronic Engineering, The Chinese University of Hong Kong

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  1. Anisotropic Double Cross Search Algorithm using Multiresolution-Spatio-Temporal Context for Fast Lossy In-Band Motion Estimation Yu Liu and King Ngi Ngan Department of Electronic Engineering, The Chinese University of Hong Kong PCS2006, April 24-26, Beijing, China

  2. Outline • Introduction • Background • Proposed Algorithm • Experimental Results • Conclusion

  3. 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)

  4. 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)

  5. 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

  6. 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:

  7. BackgroundAnisotropic Motion Model in Wavelet Domain • Traditional 2D ME in spatial domain • suffers from the aperture problem • 2D ME in wavelet domain • the aperture problem can in fact be exploited as an advantage. (a) Aperture problem in spatial domain, (b) Anisotropic motion model in wavelet domain

  8. Proposed AlgorithmMultiresolution-Spatio-Temporal Context (1) • Traditional MRME algorithms • Multiresolution context • Not enough for reducing the risk of getting trapped into a local minimum. • The proposed algorithm • Multiresolution-spatio-temporal Context • Consists of one multiresolution context, four spatial contexts, and five temporal contexts. (a) multiresolution context, (b) spatial context, (c) temporal context

  9. Proposed AlgorithmMultiresolution-Spatio-Temporal Context (2) • For LL subband • Initialization: spatio-temporal context, plus the candidate points in shifted LL subband, where the median predictor is located • Refinement:diamond search algorithm • For other levels • Initialization: multiresolution-spatio-temporal context • Refinement: anisotropic double cross search algorithm

  10. Proposed AlgorithmAnisotropic Double Cross Search Algorithm (1) • Anisotropic motion model suggests that the 2D ME problem in wavelet domain can be approximated by 1D ME along the normal flow direction for the vertical/horizontal subbands. • During the 1D window searching, only the coefficients in the corresponding subbands and LL subband are computed.

  11. Proposed AlgorithmAnisotropic Double Cross Search Algorithm (2)

  12. Experimental Results (1) Simulation results are reported in the following ways: • PSNR • MAD • operation number • speed-up ratio • For performance comparison • Full Search Algorithm (FSA) • FMRME [6] • FIBME [7] • proposed MR-STC-ADCS

  13. Experimental Results (2) • Comparison of the Tested Algorithms for QCIF Video Sequences

  14. Experimental Results (3) • Comparison of the Tested Algorithms for CIF Video Sequences

  15. Experimental Results (4) • Comparison of the Tested Algorithms for 4CIF Video Sequences On average, for all sequences examined in the experimental tests: MR-STC-ADCS is roughly 11.5 and 2.6 times faster whereas its PSNR is approximately 1.46 dB and 0.6 dB higher than FMRME and FIBME; and its MAD is approximately 0.426 and 0.165 lower than FMRME and FIBME. MR-STC-ADCS is about 271 times faster than FSA for QCIF, 667 times for CIF, and 1313 times for 4CIF, while having an average PSNR loss of only 0.04 dB or an average MAD increase of only 0.018 compared to the FSA.

  16. Conclusion • Fast Lossy In-Band Motion Estimation Algorithm • Anisotropic property of the motion field in shift-invariant wavelet domain • Multiresolution-spatio-temporal Context • Anisotropic Double Cross Search

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