# Efficient Adaptation of Neural Network Filter for Video Compression

arXiv: https://arxiv.org/abs/2007.14267 · HTML: https://mlsanity.com/paper/2007.14267/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [Overfitting for Fun and Profit: Instance-Adaptive Data Compression](https://mlsanity.com/paper/2101.08687/) (2021)
- [Efficient Video Compression via Content-Adaptive Super-Resolution](https://mlsanity.com/paper/2104.02322/) (2021)
- [Learned Image Coding for Machines: A Content-Adaptive Approach](https://mlsanity.com/paper/2108.09992/) (2021)
- [Group-aware Parameter-efficient Updating for Content-Adaptive Neural Video Compression](https://mlsanity.com/paper/2405.04274/) (2024)
- [Universal Deep Image Compression via Content-Adaptive Optimization with Adapters](https://mlsanity.com/paper/2211.00918/) (2022)
- [Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression](https://mlsanity.com/paper/2308.07733/) (2023)
- [Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression](https://mlsanity.com/paper/2209.00964/) (2022)
- [Adaptation and Attention for Neural Video Coding](https://mlsanity.com/paper/2112.08767/) (2021)
- [Test-time adaptation for image compression with distribution regularization](https://mlsanity.com/paper/2410.12191/) (2024)
- [NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines](https://mlsanity.com/paper/2401.10761/) (2024)
- [Multi-Density Attention Network for Loop Filtering in Video Compression](https://mlsanity.com/paper/2104.12865/) (2021)
- [Prediction-Aware Quality Enhancement of VVC Using CNN](https://mlsanity.com/paper/2112.04225/) (2021)
- [Frequency-Aware Re-Parameterization for Over-Fitting Based Image Compression](https://mlsanity.com/paper/2310.08068/) (2023)
- [Deep Learning-based Filtering for Video Coding: A Survey on Architectures, Algorithms, and Complexity Analysis](https://mlsanity.com/paper/2607.16319/) (2026)

## Abstract (arXiv)

We present an efficient finetuning methodology for neural-network filters which are applied as a postprocessing artifact-removal step in video coding pipelines. The fine-tuning is performed at encoder side to adapt the neural network to the specific content that is being encoded. In order to maximize the PSNR gain and minimize the bitrate overhead, we propose to finetune only the convolutional layers' biases. The proposed method achieves convergence much faster than conventional finetuning approaches, making it suitable for practical applications. The weight-update can be included into the video bitstream generated by the existing video codecs. We show that our method achieves up to 9.7% average BD-rate gain when compared to the state-of-art Versatile Video Coding (VVC) standard codec on 7 test sequences.