Work

Image Compression Model Based on Dynamic Convolution and Vision Mamba

Year: 2025

Type: article

Abstract: ABSTRACT We propose an efficient image compression scheme leveraging Vision Mamba and dynamic convolution, addressing the limitations of existing methods, such as failure to capture long‐range pixel dependencies and high computational complexity. Our approach improves both global and local information learning with reduced computational cost. Experimental results on the Kodak, Tecnick and CLIC datasets show that our model achieves competitive performance with lower algorithm complexity. Our code is available on: https://github.com/Lynxsx/ICVM .

Source: IET Image Processing

DOI: 10.1049/ipr2.70080

Authors: Lingchen Qiu, Enjian Bai, Yun Wu, Yuwen Cao, Xue‐qin Jiang

Institution: Donghua University

Language: English

Topic: Advanced Data Compression Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,740

Funder: National Natural Science Foundation of China