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     Research Journal of Applied Sciences, Engineering and Technology


Mosaic Defect Detection Based on Macro Block Solid Edge Detection

1Zhenhua Wei, 1Jie Lin, 1Le Zhang and 2Shibo Song
1College of Control and Computer Engineering, North China Electric Power University, Beijing102206, China
2Department of Mathematics and Physics, Harbin College, Harbin 15001, China
Research Journal of Applied Sciences, Engineering and Technology  2013  13:3549-3553
http://dx.doi.org/10.19026/rjaset.5.4486  |  © The Author(s) 2013
Received: July 27, 2012  |  Accepted: September 17, 2012  |  Published: April 15, 2013

Abstract

Mosaic defect detection is a common phenomenon of video defect in Digital video and will degrade the video quality. Therefore, it is more and more important to locate the mosaic and restore the video. Since the macro block is the smallest synchronization unit in the video image and mosaic is occurred in the form of macro block. A mosaic block detection method based on macro block edge detection is proposed in this study. First, the frame image is processed by canny edge detection algorithm to obtain the edge image. Second, the binary edge image is traversed by macro block and determines the solid edge of macro block; finally the mosaic block is determined according to the number of solid edges on every macro block. The experimental results show that the algorithm has good performance.

Keywords:

Edge detection, mosaic detection, video defect,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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