Convolutional Transformer-Based Image Compression
- Authors
- Type
- Preprint
- Publication Date
- Sep 06, 2024
- Submission Date
- Sep 06, 2024
- Identifiers
- DOI: 10.23919/SPA59660.2023.10274433
- Source
- arXiv
- License
- Green
- External links
Abstract
In this paper, we present a novel transformer-based architecture for end-to-end image compression. Our architecture incorporates blocks that effectively capture local dependencies between tokens, eliminating the need for positional encoding by integrating convolutional operations within the multi-head attention mechanism. We demonstrate through experiments that our proposed framework surpasses state-of-the-art CNN-based architectures in terms of the trade-off between bit-rate and distortion and achieves comparable results to transformer-based methods while maintaining lower computational complexity.
