  Hyperspectral images, which record the electromagnetic spectrum for a pixel
in the image of a scene, often store hundreds of channels per pixel and contain
an order of magnitude more information than a similarly-sized RBG color image.
Consequently, concomitant with the decreasing cost of capturing these images,
there is a need to develop efficient techniques for storing, transmitting, and
analyzing hyperspectral images. This paper develops a method for hyperspectral
image compression using implicit neural representations where a multilayer
perceptron network F with sinusoidal activation functions "learns" to map pixel
locations to pixel intensities for a given hyperspectral image I. F thus acts
as a compressed encoding of this image, and the original image is reconstructed
by evaluating F at each pixel location. We use a sampling method with two
factors: window size and sampling rate to reduce the compression time. We have
evaluated our method on four benchmarks -- Indian Pines, Jasper Ridge, Pavia
University, and Cuprite using PSNR and SSIM -- and we show that the proposed
method achieves better compression than JPEG, JPEG2000, and PCA-DCT at low
bitrates. Besides, we compare our results with the learning-based methods like
PCA+JPEG2000, FPCA+JPEG2000, 3D DCT, 3D DWT+SVR, and WSRC and show the
corresponding results in the "Compression Results" section. We also show that
our methods with sampling achieve better speed and performance than our method
without sampling.
