Resolution-Flexible Decoding for Hybrid Neural Video Representations
Neural video representations (NVRs) represent videos using neural network parameters and, in hybrid formulations, frame-wise latent embeddings. Although hybrid NVRs can improve reconstruction quality by using content-adaptive latent embeddings, their latent spatial sizes and decoder upsampling schedules are tied to the target frame resolution. For high-resolution videos, this dependency may require large and non-uniform upsampling factors and can affect the parameter allocation between the latent embeddings and the decoder. In this paper, we propose a resolution-flexible decoder framework for hybrid NVRs. The decoder is constructed from uniform \(2\times\) upsampling stages, whose target ...
| Authors | Taiga Hayami, Masaya Takabe, Hiroshi Watanabe |
|---|---|
| Primary category | eess.IV |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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