Hybrid motion estimation scheme for secondary SP-frame coding using inter-frame correlation and FMO

Ki Kit Lai, Yui Lam Chan, Chang Hong Fu, Wan Chi Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


To cope with considerable size of secondary SP-frames, quantized-transform domain motion estimation has recently been proved to be appropriate for the coding of secondary SP-frames in H.264/AVC. Nevertheless, its computational complexity is tremendous and there are still some situations that pixel-domain motion estimation can perform better. Both techniques are therefore not implemented solely in secondary SP-frame coding. In this paper, a hybrid scheme is proposed to effectively combine two existing motion estimation techniques. The combination is based on a new measurement of inter-frame correlation using the bit-counts of the macroblocks in SP-frames, so that the hybrid scheme is dominated by employing quantized-transform domain motion estimation in the macroblocks with weaker inter-frame correlation; otherwise, it approaches to pixel-domain motion estimation. With the further help of the explicit mode in Flexible Macroblock Ordering (FMO), the proposed hybrid scheme classifies MBs into two slice groups by examining the domain used in motion estimation prior to coding motion vectors in a secondary SP-frame. The slice structure of a secondary SP-frame using the explicit FMO mode is flexible and can be changed during the encoding of each new frame. Simulation results show that our proposed scheme overwhelmingly outperforms the quantized-transform domain motion estimation scheme. As a consequence, the size of secondary SP-frames can be reduced remarkably with significant computational reduction.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalSignal Processing: Image Communication
Issue number1
Publication statusPublished - 1 Jan 2012


  • Bitstream switching
  • FMO
  • Motion estimation
  • QDCT domain
  • SP-frame

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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