# Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks

arXiv: https://arxiv.org/abs/2009.14754 · HTML: https://mlsanity.com/paper/2009.14754/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Preprocessing Enhanced Image Compression for Machine Vision](https://mlsanity.com/paper/2206.05650/) (2022)
- [Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling](https://mlsanity.com/paper/2303.02353/) (2023)
- [Real-time 6K Image Rescaling with Rate-distortion Optimization](https://mlsanity.com/paper/2304.01064/) (2023)
- [Plug-and-Play Tri-Branch Invertible Block for Image Rescaling](https://mlsanity.com/paper/2412.13508/) (2024)
- [Video Compression with Arbitrary Rescaling Network](https://mlsanity.com/paper/2306.04202/) (2023)
- [SCALED : Surrogate-gradient for Codec-Aware Learning of Downsampling in ABR Streaming](https://mlsanity.com/paper/2602.00198/) (2026)
- [CIC: Circular Image Compression](https://mlsanity.com/paper/2407.15870/) (2024)
- [Rate-distortion Optimized Point Cloud Preprocessing for Geometry-based Point Cloud Compression](https://mlsanity.com/paper/2508.01633/) (2025)
- [Joint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression](https://mlsanity.com/paper/2603.17408/) (2026)
- [A Projection-Based Surrogate Gradient Interpretation for Neural Codec Wrappers](https://mlsanity.com/paper/2606.20671/) (2026)
- [Differentiable Proxy Learning for Adaptive Quantization Control in H.264 Video Coding](https://mlsanity.com/paper/2607.10478/) (2026)

## Abstract (arXiv)

To enhance image compression performance, recent deep neural network-based research can be divided into three categories: a learnable codec, a postprocessing network, and a compact representation network. The learnable codec has been designed for an end-to-end learning beyond the conventional compression modules. The postprocessing network increases the quality of decoded images using an example-based learning. The compact representation network is learned to reduce the capacity of an input image to reduce the bitrate while keeping the quality of the decoded image. However, these approaches are not compatible with the existing codecs or not optimal to increase the coding efficiency. Specifically, it is difficult to achieve optimal learning in the previous studies using the compact representation network, due to the inaccurate consideration of the codecs. In this paper, we propose a novel standard compatible image compression framework based on Auxiliary Codec Networks (ACNs). ACNs are designed to imitate image degradation operations of the existing codec, which delivers more accurate gradients to the compact representation network. Therefore, the compact representation and the postprocessing networks can be learned effectively and optimally. We demonstrate that our proposed framework based on JPEG and High Efficiency Video Coding (HEVC) standard substantially outperforms existing image compression algorithms in a standard compatible manner.