Improved deep learning image compression model: performance optimization based on convolutional modules and local attention mechanism

Abstract

Deep learning image compression, using neural networks, improves compression over traditional methods like JPEG. These methods enhance visual quality at lower bit rates by learning better image representations. However, they struggle with capturing broad context compared to local features. To address this, we propose enhancements: a new convolutional module with stacked layers and advanced operations, and a spatial attention block ("Shuffle attention") for better feature extraction. These boost performance. Our method is faster and requires fewer parameters than state-of-the-art techniques on Kodak and CLIC datasets. Despite slightly lower rate-distortion performance, our Composite Conv module and spatial attention block effectively extract global features and reduce encoding time. In conclusion, our work advances deep learning image compression by mitigating convolutional network limitations, enhancing compression efficiency while preserving quality.