# Implicit Neural Representation for Videos Based on Residual Connection

arXiv: https://arxiv.org/abs/2407.06164 · HTML: https://mlsanity.com/paper/2407.06164/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021)
- [HNeRV: A Hybrid Neural Representation for Videos](https://mlsanity.com/paper/2304.02633/) (2023)

## Built on by (most cited first, 3 of 3)

- [Neural Video Representation for Redundancy Reduction and Consistency Preservation](https://mlsanity.com/paper/2409.18497/) (2024)
- [SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution](https://mlsanity.com/paper/2505.00046/) (2025)
- [Structure-Preserving Patch Decoding for Efficient Neural Video Representation](https://mlsanity.com/paper/2506.12896/) (2025)

## Abstract (arXiv)

Video compression technology is essential for transmitting and storing videos. Many video compression methods reduce information in videos by removing high-frequency components and utilizing similarities between frames. Alternatively, the implicit neural representations (INRs) for videos, which use networks to represent and compress videos through model compression. A conventional method improves the quality of reconstruction by using frame features. However, the detailed representation of the frames can be improved. To improve the quality of reconstructed frames, we propose a method that uses low-resolution frames as residual connection that is considered effective for image reconstruction. Experimental results show that our method outperforms the existing method, HNeRV, in PSNR for 46 of the 49 videos.