Paper Info
Title
A switched adaptive predictor for lossless compression of high resolution images
Abstract
Modern switched adaptive predictors such as gradient adjusted predictor (GAP) estimates the slope of pixels from the prediction context of a unknown pixel. Based on this slope, the unknown pixels is predicted. But slope alone can not characterize some of the more complex relationship between the predicted pixel and its prediction context. In this work, this complex relationship is found in terms of a statistically valid sixth order predictor, which is used for predicting pixels under various slope conditions in the GAP frame work. It is seen through simulations, that the average entropy of the residual images is reduced significantly, when applied on high resolution images. The computational cost of the proposed method is almost of the same order as that of the GAP and requires the same previous two line buffering while coding.
Year
DOI
Venue
2005
10.1109/ICC.2005.1494517
Communications, 2005. ICC 2005. 2005 IEEE International Conference
Keywords
Field
DocType
adaptive signal processing,data compression,gradient methods,image coding,image resolution,prediction theory,average entropy,gradient adjusted predictor estimation,high resolution image,lossless compression,switched adaptive predictor
Real-time computing,Artificial intelligence,Adaptive filter,Adaptive coding,Residual,Computer vision,Algorithm,Pixel,Data compression,Image resolution,Image compression,Mathematics,Lossless compression
Conference
Volume
ISSN
ISBN
2
1550-3607
0-7803-8938-7
Citations 
PageRank 
References 
3
0.56
10
Authors
2
Name
Order
Citations
PageRank
Anil Kumar Tiwari16517.51
Ratnam V. Raja Kumar2112.41