ScoutNeRV: Rapid Encoding of Grid-Based Video INRs via ScoutNet

Abstract

Implicit neural representations (INRs) have emerged as a promising paradigm for video compression, providing compact neural representations with flexible spatial and temporal reconstruction. Hierarchical grid-based architectures such as HiNeRV achieve strong rate--distortion performance, but require extensive per-video optimization, resulting in high encoding costs. To address this limitation, we propose ScoutNeRV, a content-adaptive initialization framework for accelerating the optimization of hierarchical video INRs. ScoutNeRV employs a lightweight, offline-trained scout network that analyzes a small number of sampled frames and selects a suitable pre-trained expert from a memory bank through hard routing. The hierarchical grid and decoder parameters of the selected expert are then transferred to initialize the target HiNeRV model before video-specific fine-tuning. On the unseen ReadySetGo sequence, ScoutNeRV achieves an initial PSNR of $34.95$~dB, compared with $13.70$~dB for standard initialization, corresponding to a $21.25$~dB improvement before fine-tuning. After only 37 epochs, ScoutNeRV reaches $36.92$~dB and remains within $0.42$--$0.80$~dB of the 300-epoch HiNeRV baseline across the evaluated rate--distortion configurations. Furthermore, the proposed initialization achieves a $9.25\times$ wall-clock speedup in the reported runtime experiment. These results demonstrate that content-aware expert initialization can substantially reduce the optimization cost of hierarchical video INRs while retaining competitive reconstruction and compression performance. The code is available at https://github.com/nasserdeveloper/ScoutNeRV.