AI 中文总结
本研究提出一种基于隐式神经表示的高光谱视频压缩扩展方法，相比传统逐帧压缩，显著提升重建质量与下游目标跟踪性能。
AI 中文摘要
随着快照相机的出现，高光谱视频变得更加易于获取。近年来，新应用的出现导致了数据集规模日益增大。然而，高光谱视频压缩仍处于早期阶段。在本研究中，我们探索使用隐式神经表示作为候选解决方案。我们提出了一种对现有RGB视频压缩模型的新颖扩展，与逐帧应用的传统高光谱图像压缩方法相比，实现了Bjøntegaard Delta PSNR增益+4.99 dB和Bjøntegaard Delta码率-88.88%。除了重建质量外，还以目标跟踪成功率的形式衡量了对下游任务性能的影响。与基于主成分分析和JPEG2000的低数据量压缩方法相比，我们提出的方法在HOT2026数据集上的示例中将跟踪曲线下面积提高了最多23.42%，距离精度提高了最多35.56%。
英文摘要
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 Bjøntegaard Delta PSNR gains of +4.99 dB and Bjøntegaard Delta rate of -88.88% compared to traditional hyperspectral image compression methods applied frame-by-frame. In addition to reconstruction quality, the effects on downstream task performance are measured in the form of object tracking success. Compared to video compressed with methods based on principal component analysis and JPEG2000 in low data regimes, our proposed method improves tracking area under the curve by up to 23.42% and distance precision by up to 35.56% on examples from the HOT2026 dataset.
CommentsAccepted at IEEE WHISPERS 2026
