NDLI: Edge detection for fast block-matching motion estimation to enhance Mean Predictive Block Matching algorithm

Content Provider	IEEE Xplore Digital Library
Author	Ahmed, Z. Hussain, A.J. Al-Jumeily, D.
Copyright Year	2012
Description	Author affiliation: School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK (Ahmed, Z.; Hussain, A.J.; Al-Jumeily, D.)
Abstract	This paper presents a new technique called edge detection for fast block-matching motion estimation. In order to find the matching macroblock for the current macroblock from previous frame, this technique classifies the current macroblock into shade and edge. The shade macroblock has a high probability to move in the same direction as its neighbouring macroblocks. This property has been used to decrease the computations of Mean Predictive Block Matching algorithm. The proposed technique used only the motion vectors of the neighbouring macroblocks and ignore other motion vectors that were utilized in the first search step of Mean Predictive Block Matching algorithm. Experimental results of various video sequences types showed that the proposed technique reduces the average number of search points required per macroblock for the videos, as well as keeps or enhances the resolution of some decompressed videos in comparison to the Mean Predictive Block Matching algorithm and the standard block matching algorithms.
Starting Page	1
Ending Page	5
File Size	878759
Page Count	5
File Format	PDF
ISBN	9781467314466
e-ISBN	9781467314480
DOI	10.1109/INISTA.2012.6247015
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motion estimation Image edge detection Signal processing algorithms Block-matching algorithm Video compression Prediction algorithms Vectors Diamond-like carbon Motion compensation Edge detection
Content Type	Text
Resource Type	Article

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