EICNet: An End-to-End Efficient Learning-Based Image Compression Network

Abstract

In the era of large-scale data, the role of image compression in computer vision(CV) and computer graphics(CG) tasks is increasingly critical. Traditional methods of image compression have reached their potential limits, leading to increased interest in deep learning-based techniques. However, these modern methods often compromise image quality and require extensive decoding times. This paper introduces the EICNet, which features the innovative Quick Depth-Residual Attention Module (Q-DRAM), an optimized post-processing module, and a checkerboard context model. This design aims to overcome typical shortcomings of deep learning-based compression, enhancing both training and compression efficiency as well as the quality of images at equivalent bit rates. The findings suggest that EICNet improves both the quality and efficiency of image compression. This approach marks a significant advancement in image compression technology, potentially benefiting future applications in the field. The code for this research can be accessed at: https://github.com/ziyicheng427/EICNet.