# Standard compliant video coding using low complexity, switchable neural wrappers

arXiv: https://arxiv.org/abs/2407.07395 · HTML: https://mlsanity.com/paper/2407.07395/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Deep Contextual Video Compression](https://mlsanity.com/paper/2109.15047/) (2021)
- [Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression](https://mlsanity.com/paper/2207.05894/) (2022)
- [Image and Video Compression with Neural Networks: A Review](https://mlsanity.com/paper/1904.03567/) (2019)
- [VCT: A Video Compression Transformer](https://mlsanity.com/paper/2206.07307/) (2022)
- [Learning End-to-End Lossy Image Compression: A Benchmark](https://mlsanity.com/paper/2002.03711/) (2020)
- [EVC: Towards Real-Time Neural Image Compression with Mask Decay](https://mlsanity.com/paper/2302.05071/) (2023)
- [A Technical Overview of AV1](https://mlsanity.com/paper/2008.06091/) (2020)
- [MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering](https://mlsanity.com/paper/2007.07099/) (2020)
- [Deep Learning-Based Video Coding: A Review and A Case Study](https://mlsanity.com/paper/1904.12462/) (2019)
- [Better Compression with Deep Pre-Editing](https://mlsanity.com/paper/2002.00113/) (2020)
- [Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers](https://mlsanity.com/paper/2303.11473/) (2023)
- [How to Exploit the Transferability of Learned Image Compression to Conventional Codecs](https://mlsanity.com/paper/2012.01874/) (2020)

## Built on by (most cited first, 2 of 2)

- [Versatile Recompression-Aware Perceptual Image Super-Resolution](https://mlsanity.com/paper/2511.18090/) (2025)
- [Deformable 2D Gaussian Splatting for Efficient 4K Video Compression](https://mlsanity.com/paper/2609.14129/) (2026)

## Abstract (arXiv)

The proliferation of high resolution videos posts great storage and bandwidth pressure on cloud video services, driving the development of next-generation video codecs. Despite great progress made in neural video coding, existing approaches are still far from economical deployment considering the complexity and rate-distortion performance tradeoff. To clear the roadblocks for neural video coding, in this paper we propose a new framework featuring standard compatibility, high performance, and low decoding complexity. We employ a set of jointly optimized neural pre- and post-processors, wrapping a standard video codec, to encode videos at different resolutions. The rate-distorion optimal downsampling ratio is signaled to the decoder at the per-sequence level for each target rate. We design a low complexity neural post-processor architecture that can handle different upsampling ratios. The change of resolution exploits the spatial redundancy in high-resolution videos, while the neural wrapper further achieves rate-distortion performance improvement through end-to-end optimization with a codec proxy. Our light-weight post-processor architecture has a complexity of 516 MACs / pixel, and achieves 9.3% BD-Rate reduction over VVC on the UVG dataset, and 6.4% on AOM CTC Class A1. Our approach has the potential to further advance the performance of the latest video coding standards using neural processing with minimal added complexity.