# Processing Energy Modeling for Neural Network Based Image Compression

arXiv: https://arxiv.org/abs/2306.16755 · HTML: https://mlsanity.com/paper/2306.16755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2023.

## Built on by (most cited first, 1 of 1)

- [SLIC: A Learned Image Codec Using Structure and Color](https://mlsanity.com/paper/2401.17246/) (2024)

## Abstract (arXiv)

Nowadays, the compression performance of neural-networkbased image compression algorithms outperforms state-of-the-art compression approaches such as JPEG or HEIC-based image compression. Unfortunately, most neural-network based compression methods are executed on GPUs and consume a high amount of energy during execution. Therefore, this paper performs an in-depth analysis on the energy consumption of state-of-the-art neural-network based compression methods on a GPU and show that the energy consumption of compression networks can be estimated using the image size with mean estimation errors of less than 7%. Finally, using a correlation analysis, we find that the number of operations per pixel is the main driving force for energy consumption and deduce that the network layers up to the second downsampling step are consuming most energy.