Paper Info
Title
Prediction with Partial Match using two-dimensional approximate contexts.
Abstract
The Prediction with Partial Match (PPM) is a context-based lossless compression scheme developed in the mid 80's. Originally it was targeted towards compressing text that can be viewed as a one-dimensional sequence of symbols. When compressing digital images, PPM usually breaks the two-dimensional data into a one-dimensional raster scan form. This paper extends PPM in order to take full advantage of the two-dimensional nature of digital images. Unlike the traditional two-dimensional raster scan contexts (i.e. concerning upper pixels and pixels to the left), the proposed context is determined using pixels from all directions, including pixels to the right and the lower pixels. Results show that this type of context yields a significant improvement over the traditional raster scan context.
Year
DOI
Venue
2012
10.1109/PCS.2012.6213322
PCS
Keywords
Field
DocType
data compression,image coding,PPM,compressing text,context-based lossless compression,digital image compression,one-dimensional raster,pixels,prediction with partial match,symbol one-dimensional sequence,two-dimensional approximate contexts,two-dimensional data,two-dimensional raster scan contexts,Approximate context,Context-based schemes,Image encoding,Lossless compression,Prediction with Partial Match (PPM),Two-dimensional compression
Computer vision,Lossy compression,Computer science,Color Cell Compression,Digital image,Theoretical computer science,Prediction by partial matching,Raster scan,Pixel,Artificial intelligence,Data compression,Lossless compression
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
2
Name
Order
Citations
PageRank
Ishtiaque Hossain111.42
Mahmoud R. El-Sakka28114.17