Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Fast block prediction–based coding of compound images by exploiting edge orientation

Fast block prediction–based coding of compound images by exploiting edge orientation Compound images are a combination of text, graphics and natural images. This paper presents a new fast block prediction algorithm for coding of text/graphics parts which exploits the direction of edges in the text/graphics blocks. The proposed scheme classifies the text/graphics blocks into four kinds of blocks using edge characteristics of transformed coefficients of blocks. This edge–based block classification improves the speed of intra–block prediction mode decisions significantly. Experimental results show that the proposed method improves the value of PSNR more significantly than standard JPEG, JPEG–2000 and H.264/AVC–I, while keeping competitive compression ratio and visually lossless quality of text information. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Fast block prediction–based coding of compound images by exploiting edge orientation

Loading next page...
 
/lp/inderscience-publishers/fast-block-prediction-based-coding-of-compound-images-by-exploiting-yBfQcjGAF0

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2013.053417
Publisher site
See Article on Publisher Site

Abstract

Compound images are a combination of text, graphics and natural images. This paper presents a new fast block prediction algorithm for coding of text/graphics parts which exploits the direction of edges in the text/graphics blocks. The proposed scheme classifies the text/graphics blocks into four kinds of blocks using edge characteristics of transformed coefficients of blocks. This edge–based block classification improves the speed of intra–block prediction mode decisions significantly. Experimental results show that the proposed method improves the value of PSNR more significantly than standard JPEG, JPEG–2000 and H.264/AVC–I, while keeping competitive compression ratio and visually lossless quality of text information.

Journal

International Journal of Signal and Imaging Systems EngineeringInderscience Publishers

Published: Jan 1, 2013

There are no references for this article.