# An End-to-End Compression Framework Based on Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1708.00838 · HTML: https://mlsanity.com/paper/1708.00838/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 12 of 12)

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- [Continuous Patch Stitching for Block-wise Image Compression](https://mlsanity.com/paper/2502.16795/) (2025)
- [A Unified Image Preprocessing Framework For Image Compression](https://mlsanity.com/paper/2208.07110/) (2022)
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- [Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution](https://mlsanity.com/paper/2509.15029/) (2025)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images](https://mlsanity.com/paper/2104.04926/) (2021)
- [Adapting Learned Image Codecs to Screen Content via Adjustable Transformations](https://mlsanity.com/paper/2402.17544/) (2024)
- [Domain-Adaptive Deep Joint Source-Channel Coding for Image Classification](https://mlsanity.com/paper/2607.28907/) (2026)

## Abstract (arXiv)

Deep learning, e.g., convolutional neural networks (CNNs), has achieved great success in image processing and computer vision especially in high level vision applications such as recognition and understanding. However, it is rarely used to solve low-level vision problems such as image compression studied in this paper. Here, we move forward a step and propose a novel compression framework based on CNNs. To achieve high-quality image compression at low bit rates, two CNNs are seamlessly integrated into an end-to-end compression framework. The first CNN, named compact convolutional neural network (ComCNN), learns an optimal compact representation from an input image, which preserves the structural information and is then encoded using an image codec (e.g., JPEG, JPEG2000 or BPG). The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end. To make two CNNs effectively collaborate, we develop a unified end-to-end learning algorithm to simultaneously learn ComCNN and RecCNN, which facilitates the accurate reconstruction of the decoded image using RecCNN. Such a design also makes the proposed compression framework compatible with existing image coding standards. Experimental results validate that the proposed compression framework greatly outperforms several compression frameworks that use existing image coding standards with state-of-the-art deblocking or denoising post-processing methods.