One-point hexagonal inner search for fast motion estimation

Chorng Yann Su*, Cheng Tao Chang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a novel inner search algorithm for fast motion estimation to speed up the coding of video compression. The proposed algorithm, called as one-point hexagonal inner search (OPHIS), is based on the characteristic of monotonically decreasing of distortion on a local area. The most probable inner point is checked when an inner search starts. Compared with the enhanced hexagon-based search algorithm (EHEXBS), the OPHIS not only decreases the number of search points but also gets a better video quality. Experimental results show that the speed improvement is about 12.06% on average and the percentage decrease of the mean squared error is near to 2.29% on average. Therefore, the proposed OPHIS is more suitable for the applications of fast motion estimation than the EHEXBS.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - First International Symposium, ISVC 2005, Proceedings
PublisherSpringer Verlag
Pages406-412
Number of pages7
ISBN (Print)3540307508, 9783540307501
DOIs
Publication statusPublished - 2005
EventFirst International Symposium on Advances in Visual Computing, ISVC 2005 - Lake Tahoe, NV, United States
Duration: 2005 Dec 52005 Dec 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3804 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceFirst International Symposium on Advances in Visual Computing, ISVC 2005
Country/TerritoryUnited States
CityLake Tahoe, NV
Period2005/12/052005/12/07

Keywords

  • Enhanced hexagon-based search algorithm
  • Fast motion estimation
  • Hexagonal search
  • One-point hexagonal inner search

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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