# Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG

arXiv: https://arxiv.org/abs/1706.01000 · HTML: https://mlsanity.com/paper/1706.01000/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2017.

## Built on by (most cited first, 9 of 9)

- [Sparsification via Compressed Sensing for Automatic Speech Recognition](https://mlsanity.com/paper/2102.04932/) (2021)
- [Dual-Scale Transformer for Large-Scale Single-Pixel Imaging](https://mlsanity.com/paper/2404.05001/) (2024)
- [Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms](https://mlsanity.com/paper/2205.03677/) (2022)
- [Learned Lossless Compression for JPEG via Frequency-Domain Prediction](https://mlsanity.com/paper/2303.02666/) (2023)
- [Deep Network for Image Compressed Sensing Coding Using Local Structural Sampling](https://mlsanity.com/paper/2402.19111/) (2024)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications](https://mlsanity.com/paper/2103.04421/) (2021)
- [Quantization-aware Matrix Factorization for Low Bit Rate Image Compression](https://mlsanity.com/paper/2408.12691/) (2024)
- [Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation](https://mlsanity.com/paper/2502.15188/) (2025)

## Abstract (arXiv)

We present an end-to-end image compression system based on compressive sensing. The presented system integrates the conventional scheme of compressive sampling and reconstruction with quantization and entropy coding. The compression performance, in terms of decoded image quality versus data rate, is shown to be comparable with JPEG and significantly better at the low rate range. We study the parameters that influence the system performance, including (i) the choice of sensing matrix, (ii) the trade-off between quantization and compression ratio, and (iii) the reconstruction algorithms. We propose an effective method to jointly control the quantization step and compression ratio in order to achieve near optimal quality at any given bit rate. Furthermore, our proposed image compression system can be directly used in the compressive sensing camera, e.g. the single pixel camera, to construct a hardware compressive sampling system.