Enhancing Learned Image Compression via Cross Window-based Attention

TL;DR

This paper introduces a CNN-based image compression method that incorporates a feature encoding module and cross-scale window-based attention to better capture local and global redundancies, achieving competitive results.

Contribution

The paper proposes a novel CNN architecture with a feature encoding and cross-scale window-based attention modules that can be integrated into existing networks for improved image compression.

Findings

Effective in capturing local redundancy

Achieves performance comparable to state-of-the-art methods

Flexible modules that can be incorporated into other architectures

Abstract

In recent years, learned image compression methods have demonstrated superior rate-distortion performance compared to traditional image compression methods. Recent methods utilize convolutional neural networks (CNN), variational autoencoders (VAE), invertible neural networks (INN), and transformers. Despite their significant contributions, a main drawback of these models is their poor performance in capturing local redundancy. Therefore, to leverage global features along with local redundancy, we propose a CNN-based solution integrated with a feature encoding module. The feature encoding module encodes important features before feeding them to the CNN and then utilizes cross-scale window-based attention, which further captures local redundancy. Cross-scale window-based attention is inspired by the attention mechanism in transformers and effectively enlarges the receptive field. Both the feature encoding module and the cross-scale window-based attention module in our architecture are flexible and can be incorporated into any other network architecture. We evaluate our method on the Kodak and CLIC datasets and demonstrate that our approach is effective and on par with state-of-the-art methods. Our code is publicly available at https://github.com/prmudgal/CWAM_IC_ISVC. .