RT Journal Article
T1 Hyperspectral Image Compression Using Vector Quantization, PCA and JPEG2000
A1 Bascones García, Daniel
A1 González Calvo, Carlos
A1 Mozos Muñoz, Daniel
AB Compression of hyperspectral imagery increases the efficiency of image storage and transmission. It is especially useful to alleviate congestion in the downlinks of planes and satellites, where these images are usually taken from. A novel compression algorithm is presented here. It first spectrally decorrelates the image using Vector Quantization and Principal Component Analysis (PCA), and then applies JPEG2000 to the Principal Components (PCs) exploiting spatial correlations for compression. We take advantage of the fact that dimensionality reduction preserves more information in the first components, allocating more depth to the first PCs. We optimize the selection of parameters by maximizing the distortion-ratio performance across the test images. An increase of 1 to 3 dB in Signal Noise Ratio (SNR) for the same compression ratio is found over just using PCA + JPEG2000, while also speeding up compression and decompression by more than 10%. A formula is proposed which determines the configuration of the algorithm, obtaining results that range from heavily compressed-low SNR images to low compressed-near lossless ones.
PB MDPI
SN 2072-4292
YR 2018
FD 2018-06-08
LK https://hdl.handle.net/20.500.14352/12685
UL https://hdl.handle.net/20.500.14352/12685
LA eng
NO Ministerio de Economía y Competitividad (MINECO)
DS Docta Complutense
RD 13 may 2025