Hyperspectral Image Compression Using Implicit Neural Representation
This addresses storage and transmission challenges for hyperspectral images, which have hundreds of channels per pixel, but is incremental as it applies an existing method to a new domain.
The paper tackles hyperspectral image compression by using implicit neural representations to map pixel locations to intensities, achieving better compression than JPEG, JPEG2000, and PCA-DCT at low bitrates on four benchmarks.
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 typical similarly-sized 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 $Φ_θ$ with sinusoidal activation functions ``learns'' to map pixel locations to pixel intensities for a given hyperspectral image $I$. $Φ_θ$ thus acts as a compressed encoding of this image. The original image is reconstructed by evaluating $Φ_θ$ at each pixel location. We have evaluated our method on four benchmarks -- Indian Pines, Cuprite, Pavia University, and Jasper Ridge -- and we show the proposed method achieves better compression than JPEG, JPEG2000, and PCA-DCT at low bitrates.