TVRN: Invertible Neural Networks for Compression-Aware Temporal Video Rescaling
To fit diverse display and bandwidth constraints, high-frame-rate videos are temporally downscaled to low-frame-rate (LFR) and later upscaled, requiring joint optimization for effective frame-rate rescaling. However, existing methods typically link the two operations via training objectives, without fully exploiting their reciprocal nature, which may cause high-frequency information loss. Moreover, they overlook the impact of lossy codecs on LFR videos, limiting real-world applicability. In this work, we propose an end-to-end framework for compression-aware frame-rate rescaling, named TVRN. To regularize high-frequency information lost during frame-rate downscaling, TVRN adopts an inverti...
| Authors | Xinmin Feng, Li Li, Dong Liu et al. |
|---|---|
| Primary category | eess.IV |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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