Hyperspectral imaging is a powerful technique and has
been used in a large number of applications. However,
it generates massively large image data sets. Access
and transport of these data sets will stress existing
processing, storage and transmission capabilities. A
new embedded, block-based, wavelet transform coding
algorithm of low complexity is developed for
hyperspectral image compression: Three-Dimensional
Set Partitioned Embedded bloCK (3D-SPECK). 3D-SPECK
efficiently encodes 3D volumetric hyperspectral image
data by exploiting the dependencies in all
dimensions. It can generate either SNR scalable or
resolution scalable embedded bitstreams. It can also
generate ROI retrievable bitstreams.This book
introduces 3D-SPECK and describes its technical
details. It is demonstrated that 3D-SPECK has
excellent performance on hyperspectral image compression.
been used in a large number of applications. However,
it generates massively large image data sets. Access
and transport of these data sets will stress existing
processing, storage and transmission capabilities. A
new embedded, block-based, wavelet transform coding
algorithm of low complexity is developed for
hyperspectral image compression: Three-Dimensional
Set Partitioned Embedded bloCK (3D-SPECK). 3D-SPECK
efficiently encodes 3D volumetric hyperspectral image
data by exploiting the dependencies in all
dimensions. It can generate either SNR scalable or
resolution scalable embedded bitstreams. It can also
generate ROI retrievable bitstreams.This book
introduces 3D-SPECK and describes its technical
details. It is demonstrated that 3D-SPECK has
excellent performance on hyperspectral image compression.