Current learned image compression models typically exhibit high complexity, which demands significant computational resources. To overcome these challenges, we propose an innovative approach that employs hierarchical feature extraction transforms to significantly reduce complexity while preserving bit rate reduction efficiency. Our novel architecture achieves this by using fewer channels for high spatial resolution inputs/feature maps. On the other hand, feature maps with a large number of channels have reduced spatial dimensions, thereby cutting down on computational load without sacrificing performance. This strategy effectively reduces the forward pass complexity from \(1256 \, \text{kMAC/Pixel}\) to just \(270 \, \text{kMAC/Pixel}\). As a result, the reduced complexity model can open the way for learned image compression models to operate efficiently across various devices and pave the way for the development of new architectures in image compression technology.
View on arXiv@article{ameen2025_2504.21778,
title={ LoC-LIC: Low Complexity Learned Image Coding Using Hierarchical Feature Transforms },
author={ Ayman A. Ameen and Thomas Richter and André Kaup },
journal={arXiv preprint arXiv:2504.21778},
year={ 2025 }
}