Paper Info
Title
Optimal slope bin classification in gradient adjusted predictor for lossless compression of medical images
Abstract
Gradient adjusted predictor (GAP) uses seven fixed range of slope quantization bins and different predictors associated with each bin, for prediction of pixels of all kinds of images. Criteria for range of slope in the bins and associated predictors are not reported in the literature. This paper presents a technique for slope quantization bins which are optimum for a given set of images. It also presents a technique for finding a statistically optimal predictor for a given range of slope bin. Simulation results, for medical images, using optimal slope bins and associated predictors show a significant better compression performance as compared to the other methods such as GAP and edge-directed prediction (EDP) method. The proposed method and GAP has same order of computational complexity while EDP is computationally much expensive.
Year
DOI
Venue
2005
10.1109/ICIP.2005.1530046
Image Processing, 2005. ICIP 2005. IEEE International Conference
Keywords
Field
DocType
computational complexity,data compression,gradient methods,image classification,image coding,medical image processing,computational complexity,edge-directed prediction method,gradient adjusted predictor,lossless compression,medical images,optimal slope bin classification,pixels prediction,slope quantization bins
Compression (physics),Computer vision,Bin,Pattern recognition,Computer science,Artificial intelligence,Pixel,Contextual image classification,Quantization (signal processing),Data compression,Lossless compression,Computational complexity theory
Conference
Volume
ISSN
ISBN
2
1522-4880
0-7803-9134-9
Citations 
PageRank 
References 
2
0.61
4
Authors
2
Name
Order
Citations
PageRank
Anil Kumar Tiwari16517.51
Ratnam V. Raja Kumar2112.41