Hyperspectral Image Compression Using Implicit Neural Representation

• URL: http://arxiv.org/abs/2302.04129v2
• Date: Thu, 9 Feb 2023 03:51:20 GMT
• Title: Hyperspectral Image Compression Using Implicit Neural Representation
• Authors: Shima Rezasoltani, Faisal Z. Qureshi
• Abstract summary: This paper develops a method for hyperspectral image compression using implicit neural representations. We show the proposed method achieves better compression than JPEG, JPEG2000, and PCA-DCT at lows.
• Score: 1.4721615285883425
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 $\Phi_\theta$ with sinusoidal activation functions ``learns'' to map pixel locations to pixel intensities for a given hyperspectral image $I$. $\Phi_\theta$ thus acts as a compressed encoding of this image. The original image is reconstructed by evaluating $\Phi_\theta$ 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.

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