Multiple weighted prediction models for video coding with brightness variations

S. H. Tsang, Yui Lam Chan, W. C. Siu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


Weighted prediction (WP) is a tool introduced in H.264 to improve motion-compensation performance for video sequences with brightness variations. Various WP models to estimate the parameter set have been discussed in the literature. However, no single WP model works well for all types of brightness variations. A single reference frame multiple WP models (SRefMWP) scheme is proposed to facilitate the use of multiple WP models in a single reference frame. The proposed scheme makes a new arrangement of the multiple frame buffers in multiple reference frame motion estimation. It enables different macroblocks in the same frame to use different WP models even when they are predicted from the same reference frame. Furthermore, a new re-ordering mechanism for SRefMWP is also proposed to guarantee that the list of the reference picture is in the best order for further decreasing the bit rate. To reduce the implementation cost, a reduction in the memory requirement is achieved via look-up tables (LUTs). Experimental results show that SRefMWP can achieve significant coding gain in scenes with different types of brightness variations. Furthermore, SRefMWP with LUTs can reduce the memory requirement by about 80% while keeping the same coding efficiency as SRefMWP.
Original languageEnglish
Pages (from-to)434-443
Number of pages10
JournalIET Image Processing
Issue number4
Publication statusPublished - 1 Jun 2012

ASJC Scopus subject areas

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


Dive into the research topics of 'Multiple weighted prediction models for video coding with brightness variations'. Together they form a unique fingerprint.

Cite this