Hyperspectral Image Compression Using Sampling and Implicit Neural Representations
Bibliographic record
Abstract
Hyperspectral images record the electromagnetic spectrum, and each hyperspectral pixel often stores hundreds of channels. Consequently, a hyperspectral image contains an order of magnitude more information than a similar-sized RGB color image. 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 article develops a method for hyperspectral image compression using implicit neural representations (INRs) where a multilayer perceptron (MLP) network with sinusoidal activation functions “learns” to map pixel locations to pixel spectrum for a given hyperspectral image. This representation, thus, acts as a compressed encoding of this image, and the original image is reconstructed by evaluating this network at each pixel location. We introduce a sampling scheme to achieve better compression times while keeping decoding errors low. The proposed method is evaluated on four benchmarks against 16 other schemes for hyperspectral compression, and according to the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics, the method developed in this article achieves state-of-the-art compression rates at low-bit rates. In addition, we show that the proposed sampling technique reduces encoding times.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".