# Semantic-assisted image compression

**Authors:** Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen, Xijun Xue  
**arXiv:** [2201.12599](https://arxiv.org/abs/2201.12599)  
**Date:** 2022-01-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2201.12599v1/

## Key points

- The study introduces a Semantic-Assisted Image Compression (SAIC) method to overcome limitations of traditional image compression techniques.
- SAIC focuses on semantic-level consistency to retain crucial semantic information during compression and enhance downstream AI task performance.
- A new metric called Semantic Information (SI) is proposed to measure the level of semantic distortion in compressed images.
- Experimental results show that SAIC outperforms deep learning-based and perceptual methods, achieving higher accuracy values on the STL dataset at 0.125 bits per pixel (bpp) compared to TDIC and APIC.
- SAIC not only considers human visual experience but also improves machine perception performance, making it suitable for various intelligent tasks.
- The innovative approach has potential applications in denoising and super-resolution, highlighting the importance of preserving semantic-level information in image compression processes for improved overall AI task performance.

## Summary

The study presents a novel Semantic-Assisted Image Compression (SAIC) method that addresses the limitations of traditional image compression techniques. By focusing on semantic-level consistency, the SAIC method aims to enhance downstream AI task performance by maximizing the retention of crucial semantic information during the compression process. To measure the level of semantic distortion in compressed images, a new metric called Semantic Information (SI) is proposed. Experimental results demonstrate that SAIC outperforms both deep learning-based and perceptual methods, achieving higher accuracy values on the STL dataset at 0.125 bits per pixel (bpp) compared to TDIC and APIC. This innovative approach not only considers human visual experience but also improves machine perception performance, making it suitable for various intelligent tasks. Additionally, SAIC has potential applications in denoising and super-resolution in the future. Overall, this study highlights the importance of preserving semantic-level information in image compression processes to effectively improve overall AI task performance.