נצפיית וידאו היפרספקטרלית: ייצוג נוירלי סמוי להקלה

תקציר מקורי באנגליתarXiv: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 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