TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference

2026-04-10

AI Summary

This work addresses the lack of systematic investigation into extremely compact neural video representations, as existing approaches predominantly focus on medium- to high-capacity models. The study presents TinyNeRV, a novel architecture that establishes the performance limits of minimal-scale NeRVs through integrated strategies including capacity scaling, frequency-aware knowledge distillation, and low-precision inference—encompassing both post-training quantization and quantization-aware training. These techniques collectively achieve substantial reductions in model parameters, computational cost, and memory footprint. Extensive experiments across multiple video datasets demonstrate that TinyNeRV attains an exceptional trade-off between reconstruction quality and efficiency, thereby validating the feasibility and robustness of lightweight neural video representations in resource-constrained and real-time deployment scenarios.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Implicit neural video representations encode entire video sequences within the parameters of a neural network and enable constant time frame reconstruction. Recent work on Neural Representations for Videos (NeRV) has demonstrated competitive reconstruction performance while avoiding the sequential decoding process of conventional video codecs. However, most existing studies focus on moderate or high capacity models, leaving the behavior of extremely compact configurations required for constrained environments insufficiently explored. This paper presents a systematic study of tiny NeRV architectures designed for efficient deployment. Two lightweight configurations, NeRV-T and NeRV-T+, are introduced and evaluated across multiple video datasets in order to analyze how aggressive capacity reduction affects reconstruction quality, computational complexity, and decoding throughput. Beyond architectural scaling, the work investigates strategies for improving the performance of compact models without increasing inference cost. Knowledge distillation with frequency-aware focal supervision is explored to enhance reconstruction fidelity in low-capacity networks. In addition, the impact of lowprecision inference is examined through both post training quantization and quantization aware training to study the robustness of tiny models under reduced numerical precision. Experimental results demonstrate that carefully designed tiny NeRV variants can achieve favorable quality efficiency trade offs while substantially reducing parameter count, computational cost, and memory requirements. These findings provide insight into the practical limits of compact neural video representations and offer guidance for deploying NeRV style models in resource constrained and real-time environments. The official implementation is available at https: //github.com/HannanAkhtar/TinyNeRV-Implementation.

Problem

• neural video representations
• compact models
• resource-constrained environments
• video reconstruction
• model capacity scaling

Innovation

• Neural Video Representation
• Model Compression
• Knowledge Distillation
• Low-Precision Inference
• Capacity Scaling