Noise variance adaptive successive elimination algorithm for block motion estimation: application for video surveillance

Noise variance adaptive successive elimination algorithm for block motion estimation: application for video surveillance

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In a practical video encoder, a video sequence obtained from a camera inevitably conveys noise. The noise term degrades not only image quality but also coding efficiency. Based on the statistical analysis of noise signal, a two-stage noise variance adaptive successive elimination algorithm (SEA) for block motion estimation is presented. To reduce the additional computation cost required for video noise estimation, it is embedded into the process of block matching using Kalman filtering. Simulation results demonstrate that the performance of the proposed algorithm is close to that of the SEA, whereas the computational complexity has been significantly reduced.


    1. 1)
      • Fast blocking matching algorithm based on the winner-update strategy
    2. 2)
      • Hosur, P.I., Ma, K.K.: `Motion vector field adaptive fast motion estimation', Proc. of 2nd Int. Conf. Information, Communications and Signal Processing (ICICS'99), 1999, p. 7–10
    3. 3)
      • Successive elimination algorithm for motion estimation
    4. 4)
      • A multilevel successive elimination algorithm for block matching motion estimation
    5. 5)
      • Fast full-search motion estimation based on multilevel successive elimination algorithm
    6. 6)
      • Fast full-search block matching
    7. 7)
      • Global elimination algorithm and architecture design for fast block matching motion estimation
    8. 8)
      • Fast and reliable structure-oriented video noise estimation
    9. 9)
      • Chen, W.-G.: `Noise variance adaptive sea for block motion estimation', Proc. 8th Int. Conf. on Signal Processing, 2006, p. 1488–1491
    10. 10)
      • Fast noise variance estimation
    11. 11)
      • Estimation, control, and the discrete Kalman filter

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