Paper Info
Title
Lossless Compression of Medical and Natural High Bit Depth Sparse Histogram Images.
Abstract
In this paper we overview histogram packing methods and focus on an off-line packing method, which requires encoding the original histogram along with the compressed image. For a diverse set containing medical MR, CR and CT images as well as various natural 16-bit images, we report histogram packing effects obtained for several histogram encoding methods. The histogram packing improves significantly JPEG2000 and JPEG-LS lossless compression ratios of high bit depth sparse histogram images. In case of certain medical image modalities the improvement may exceed a factor of two, which indicates that histogram packing should be exploited in medical image databases as well as in medical picture archiving and communication systems in general as it is both highly advantageous and easy to apply.
Year
DOI
Venue
2015
10.1007/978-3-319-18422-7_32
Communications in Computer and Information Science
Keywords
Field
DocType
Image processing,Lossless image compression,High bit depth images,Medical images,Sparse histogram,Histogram packing,Histogram encoding,Image coding standards,JPEG2000,JPEG-LS,DICOM
Computer vision,Histogram,Pattern recognition,Lossy compression,Computer science,Image processing,Histogram matching,Color depth,Artificial intelligence,JPEG 2000,Data compression,Lossless compression
Conference
Volume
ISSN
Citations 
521
1865-0929
0
PageRank 
References 
Authors
0.34
11
1
Name
Order
Citations
PageRank
Roman Starosolski1397.30