Vector quantization

Algorithm

Engineering

Image compression

Quantization (signal processing)

Principal component analysis

Image processing

Computer science

Compression ratio

Internal combustion engine

Pattern recognition (psychology)

Image (mathematics)

Hyperspectral imaging

Automotive engineering

JPEG 2000

Artificial intelligence

Data compression

Data compression ratio

Lossless compression

Computer vision

Remote Sensing

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.

Hyperspectral Image Compression Using Vector Quantization, PCA and JPEG2000