High performance, low-complexity line-based motion estimation algorithm with smoothing and preprocessing

Li Wern Chew, Wai Chong Chia, Li Minn Ang, Kah Phooi Seng

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)

    Abstract

    This paper introduces a smoothing and preprocessing (S+P) technique for a line-based one-bit-transform (1BT) motion estimation scheme. In the proposed algorithm, a smoothing threshold (ThresholdS) is incorporated into the 1BT convolutional kernel. By using the smoothing threshold, scattering noise which is a common problem in most 1BT images can be greatly reduced. After the transformation, the 1BT images for the current and reference frames are divided into a number of macroblocks. The macroblock in the current frame is first compared with the macroblock at the same position in the reference frame. If the Sum of Absolute Difference (SAD) is below a certain preprocessing threshold (ThresholdP), the macroblock in the current frame is considered to have negligible movement and motion search is not performed. Simulation results show that this technique achieves high performance and greatly reduces the number of search operations. By incorporating the S+P technique, the PSNR achieved by the 1BT is approaches the performance of the 8-bit Full Search Block Matching Algorithm (FSBMA), and the difference is as low as 0.08 dB. In addition, this technique outperforms current state-of-the-art 1BT motion estimation techniques. An improvement in PSNR performance by up to 0.6 dB and a reduction in the number of search operations by 60% to 93% is achieved using video conferencing sequences.

    Original languageEnglish
    Pages (from-to)101-114
    Number of pages14
    JournalInternational Journal of Pattern Recognition and Artificial Intelligence
    Volume23
    Issue number1
    DOIs
    Publication statusPublished - 01 Feb 2009

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