Source-linked AI summary
ScoutNeRV: Rapid Encoding of Grid-Based Video INRs via ScoutNet
Naser Alizada, Farhang Baghban, Hashem Pishkar, Ali Mousavi
TL;DR
Hierarchical video INRs such as HiNeRV offer strong rate–distortion performance but require costly per-video optimization. ScoutNeRV selects a pre-trained expert using sampled frames and transfers its parameters; on unseen ReadySetGo it improves initial PSNR by 21.25 dB and remains close to the 300-epoch HiNeRV rate–distortion results after 37 epochs.
Problem
HiNeRV's hierarchical grids achieve strong rate–distortion performance, but extensive per-video optimization makes encoding costly.
Source: §1 · p. 2, §2.2 · p. 3Method
ScoutNeRV uses a lightweight scout network to hard-select a pre-trained expert from sampled frames and transfers its grid and decoder parameters to initialize HiNeRV.
Source: §3 · p. 4Results
21.25 dB initial PSNR improvement on unseen ReadySetGo (34.95 vs 13.70 dB); after 37 epochs, ScoutNeRV is 0.42–0.80 dB from 300-epoch HiNeRV across configurations.
Source: §4.3 · p. 8Takeaways & Limitations
ScoutNeRV reduces the optimization schedule from 300 to 37 epochs while remaining close to HiNeRV's rate–distortion performance across evaluated configurations.
Source: §4.3 · p. 8ScoutNeRV accelerates initialization and fitting rather than serving as an end-to-end video codec.
Source: §1 · p. 2
Abstract
from arXiv · showhide
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.
1 Introduction
Video INRs offer compact, flexible representations, but hierarchical models such as HiNeRV remain costly to encode because each video requires extensive optimization. ScoutNeRV uses sampled frames to select a pre-trained expert and transfer its grid and decoder parameters for target-video initialization.
1 Introduction
- INRs represent video as a continuous neural function mapping temporal or spatiotemporal coordinates to RGB values, enabling compact, flexible reconstruction.The paper situates these representations amid demand for efficient compression in streaming, teleconferencing, virtual reality, and medical transmission.Source: §1 · pp. 1–2
- HiNeRV's hierarchical multiresolution grids support strong rate–distortion performance, but random per-video initialization demands extensive optimization and makes encoding costly.The cost limits applicability in latency-sensitive and rapid-turnaround scenarios.Source: §1 · p. 2
- ScoutNeRV uses a lightweight scout network to select a domain-specific expert from a memory bank based on sampled frames, then transfers its grid and decoder parameters.This replaces random initialization with a content-dependent prior for video-specific refinement.Source: §1 · p. 2
- +21.25 dB initial PSNR improvement on an unseen target sequence and up to a 9.25× wall-clock speedup are reported for ScoutNeRV.The approach is reported to maintain competitive reconstruction and rate–distortion performance.Source: §1 · p. 2
- ScoutNeRV accelerates initialization and fitting rather than providing a new end-to-end codec, and can be integrated with downstream compression stages.The paper names pruning, quantization, and entropy coding as examples.Source: §1 · p. 2
2 Background and related work
The background reviews video INRs, grid-based encoding bottlenecks, and faster initialization strategies. It motivates domain-aware expert selection because HiNeRV's structured grids pose a different transfer problem from globally organized network parameters.
2 Background and related work
- The section frames the proposed approach against limitations in video-INR initialization and motivates domain-aware expert selection for hierarchical grid-based models.It reviews INR compression, encoding challenges, and optimization acceleration methods.Source: §2 · p. 3
2.1 Implicit neural representations for video compression
- NeRV, E-NeRV, and HNeRV map temporal coordinates to frames using neural networks, but separate representations must typically be optimized from scratch for each video.That per-video training cost remains an obstacle to fast encoding.Source: §2.1 · p. 3
2.2 Grid-based INRs and encoding bottlenecks
- HiNeRV uses hierarchical multiresolution grids and lightweight decoders for rate–distortion performance, yet random initialization and independent per-video optimization require many epochs.The grids represent high-frequency spatial and temporal information.Source: §2.2 · p. 3
2.3 Fast initialization and parameter reuse
- MetaNeRV learns generalized initialization, while RNeRV and related work use parameter reuse or masking; these strategies do not directly fit HiNeRV's structured grid tensors.The section motivates transferring compatible grid and decoder parameters from optimized models.Source: §2.3 · p. 3
2.4 Domain-aware initialization for hierarchical architectures
- ScoutNet uses a few sampled frames to hard-select one pre-trained expert, whose grid and decoder initialize HiNeRV before video-specific fine-tuning.Selecting rather than combining experts avoids mixing potentially incompatible grid representations.Source: §2.4 · p. 4
3 Proposed Method
ScoutNeRV uses content from a few sampled frames to select a pre-trained HiNeRV expert, transferring its grid and decoder parameters to initialize a target video model. Offline expert storage, hard routing, and video-specific fine-tuning make up the framework’s main stages.
3 Proposed Method
- Related video content motivates transferring grid and decoder parameters from a selected pre-trained expert to initialize HiNeRV before target-specific fine-tuning.The method assumes similar videos may share transferable representation priors and preserves the original HiNeRV optimization and compression pipeline.Source: §3 · p. 4
3.1 Offline memory bank of frozen models
- The offline memory bank stores frozen HiNeRV experts trained on representative source videos from content domains such as animation, sports, or news.Each expert retains its optimized hierarchical grid and decoder parameters for reuse on an unseen target video.Source: §3.1 · pp. 4–5
3.2 Scout CNN and hard expert selection
- A lightweight scout CNN processes a few sparsely sampled, resized frames and uses top-1 hard routing to select one expert without combining model parameters.The scout is trained offline as a domain classifier, while the stored HiNeRV experts remain frozen during scout training.Source: §3.2 · p. 5
3.3 HiNeRV initialization and fast fine-tuning
- The selected expert replaces the target model’s standard initialization, after which the unchanged HiNeRV reconstruction objective is fine-tuned on the unseen video.ScoutNeRV changes only initialization; subsequent HiNeRV optimization, pruning, quantization, and compression stages remain unchanged.Source: §3.3 · p. 6
4 Experiments
Experiments compare ScoutNeRV with HiNeRV under matched compression settings and test transfer to unseen content. They report improved early reconstruction and close rate–distortion performance with substantially fewer optimization epochs.
4.1 Evaluation protocol
- The protocol keeps model configurations and downstream compression identical, while ScoutNeRV receives 37 fine-tuning epochs and HiNeRV 300 optimization epochs.BPP is computed from the final compressed models after the same compression procedure.Source: §4.1 · p. 6
4.2 Experimental setup
- Experiments use Full HD sequences from animation, news/face, and sport domains, with video-level separation between expert sources, scout training, and unseen evaluation targets.The sport expert comes from Jockey, while transfer is tested on unseen ReadySetGo.Source: §4.2 · pp. 6–7
4.3 Results and discussion
- 0.42, 0.50, and 0.80 dB are the PSNR differences between ScoutNeRV after 37 epochs and HiNeRV after 300 epochs across small, medium, and large configurations.The rate–distortion comparison uses the same compression procedure for both methods.Source: §4.1 · p. 6, Figure 4 · p. 7, §4.3 · p. 8
- On Jockey with a 30-epoch budget, ScoutNeRV reaches 39.94 dB PSNR and 0.9828 MS-SSIM, versus HiNeRV’s 32.46 dB and 0.9582.The comparison uses the same HiNeRV backbone; other-method values are published reference comparisons rather than controlled evaluations.Source: §4.3 · pp. 7–8
- On unseen ReadySetGo, ScoutNeRV starts at 34.95 dB PSNR versus 13.70 dB for conventional initialization, then reaches 36.92 dB PSNR and 0.9901 MS-SSIM after 37 epochs.The 21.25 dB initial advantage is retained when transferring the selected expert to unseen content.Source: §4.3 · p. 8
4.4.1 Effect of domain-aware expert selection
- On unseen ReadySetGo before fine-tuning, the selected sport expert starts at 34.95 dB PSNR, versus 19.20 dB for generic pre-training and 15.50 dB for a mismatched news expert.The gains are 21.25 dB over standard initialization, 19.45 dB over the wrong-domain expert, and 15.75 dB over generic pre-training.Source: §4.4.1 · pp. 8–9, Table 3 · p. 9
4.4.2 Initialization quality across video sequences
- Across source sequences, transferred experts provide higher initialization quality than standard initialization, and the 21.25 dB advantage also appears on unseen ReadySetGo before fine-tuning.The qualitative evaluations span ReadySetGo, News, Sport, and Animation sequences.Source: Table 4 · p. 9, §4.4.2 · p. 9
5 Conclusion
ScoutNeRV reduces the encoding cost of hierarchical video INRs by initializing HiNeRV with parameters from a selected pre-trained expert, without modifying its architecture. On unseen ReadySetGo, it improves initial PSNR and reaches competitive reconstruction quality with a shorter optimization schedule and a reported wall-clock speedup.
5 Conclusion
- ScoutNeRV transfers selected hierarchical grid and decoder parameters to initialize HiNeRV before video-specific fine-tuning, without changing its architecture.Source: §5 · p. 10
- 34.95 dB initial PSNR versus 13.70 dB for standard initialization yields a 21.25 dB gain on unseen ReadySetGo.Source: §5 · p. 10
- After 37 epochs, ScoutNeRV reaches 36.92 dB, staying within 0.42–0.80 dB of the 300-epoch HiNeRV baseline across evaluated rate–distortion configurations.Source: §5 · p. 10
- The reduced optimization schedule achieves a 9.25× wall-clock speedup in the reported runtime experiment.Source: §5 · p. 10
- Future work will investigate 4K/8K sequences and temporal attention for highly dynamic motion.Source: §5 · p. 10
Funding
The authors received no funding for this work.
Funding
- The authors received no funding for this work.Source: Funding · p. 10