Paper Info
Title
Model-Based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
Abstract
The inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. In this paper, we design a cost function in Bayesian framework with dictionary learning. Minimizing our cost function produces a restored image which has better quality than that of the observed noisy image, and a dictionary for representing and encoding the image. After the restoration, we use this dictionary (from the same cost function) to encode the restored image following the symbol-dictionary framework by JBIG2 standard with the lossless mode. Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio. For the test images with synthetic noise, our method reduces the number of flipped pixels by 48.2% and improves the compression ratio by 36.36% as compared with the best encoding methods. For the test images with real noise, our method visually improves the image quality, and outperforms the cutting-edge method by 28.27% in terms of the compression ratio.
Year
DOI
Venue
2017
10.1109/CVPR.2017.72
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Keywords
DocType
Volume
image quality,compression ratio,cost function,dictionary learning,restored image,observed noisy image,symbol-dictionary framework,iterative restoration,binary document image compression,JBIG2 standard,encoding methods
Conference
abs/1704.07019
Issue
ISSN
ISBN
1
1063-6919
978-1-5386-0458-8
Citations 
PageRank 
References 
2
0.36
25
Authors
4
Name
Order
Citations
PageRank
Yandong Guo125519.12
Cheng Lu220.36
Jan P. Allebach31230170.88
Charles A. Bouman42740473.62