Title
Sparse representation of dense motion vector fields for lossless compression of 4-D medical CT data
Abstract
We present a new method for data-adaptive compression of dense vector fields in dynamic medical volume data. Conventional block-based motion compensation used for temporal prediction in video compression cannot conveniently cope with deformable motion typically found in medical image sequences encoded over time. Based on an approximation of physiologic tissue motion between two succeeding slices in time direction computed by optical flow methods, we find the most significant motion vectors with respect to their prediction capability for a second 2-D slice out of the first one. By coding the components of these vectors, we are able to reconstruct a high quality dense motion vector field at the decoder using only minimal side-information. We show that our approach can achieve a smoother prediction than block-based motion compensation for such data, reducing storage demands in spatially predictive lossless compression. We also show that such a predictive approach can yield better compression ratios than JPEG 2000 intra coding.
Year
Venue
Keywords
2011
Barcelona
computerised tomography,data compression,image coding,image representation,image sequences,medical image processing,motion compensation,vectors,4-d medical ct data,data-adaptive compression,decoder,dense motion vector fields,dense vector fields,dynamic medical volume data,high quality dense motion vector field reconstruction,medical image sequences,optical flow methods,physiologic tissue motion approximation,prediction capability,sparse representation,spatially predictive lossless compression,storage demands,noise,encoding,image reconstruction,biomedical imaging,transform coding
Field
DocType
ISSN
Computer vision,Quarter-pixel motion,Lossy compression,Computer science,Motion compensation,Artificial intelligence,Motion estimation,Data compression,Image compression,Motion vector,Lossless compression
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
5
Authors
4
Name
Order
Citations
PageRank
Andreas Weinlich172.29
Peter Amon220123.28
Andreas Hutter329729.47
André Kaup4861127.24