Implicit Neural Representation for Hyperspectral Video Compression
Quick summary
arXiv:2609.31435v1 Announce Type: cross Abstract: With the advent of snapshot cameras, hyperspectral video is becoming more readily available. In recent years, new applications have emerged which have led to increasingly larger datasets. However, hyperspectral video compression remains in the early stages. In this study, we explore the use of implicit neural representation as a candidate solution. We propose a novel extension of an existing RGB video compression model, achieving Bj{\o}ntegaard Delta PSNR gains of +4.99 dB and Bj{\o}ntegaard Delta rate of -88.88% compared to traditional hypersp
Key takeaways
- arXiv:2609.31435v1 Announce Type: cross Abstract: With the advent of snapshot cameras, hyperspectral video is becoming more readily available.
- In recent years, new applications have emerged which have led to increasingly larger datasets.
- However, hyperspectral video compression remains in the early stages.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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