Variable Rate Image Compression via N-Gram Context based Swin-transformer
This work addresses image compression for industrial vision systems, offering incremental improvements in variable-rate compression.
The paper tackles variable-rate image compression by introducing an N-gram context-based Swin Transformer, achieving a -5.86% improvement in BD-Rate over existing methods and enhancing region-of-interest quality for object-focused applications.
This paper presents an N-gram context-based Swin Transformer for learned image compression. Our method achieves variable-rate compression with a single model. By incorporating N-gram context into the Swin Transformer, we overcome its limitation of neglecting larger regions during high-resolution image reconstruction due to its restricted receptive field. This enhancement expands the regions considered for pixel restoration, thereby improving the quality of high-resolution reconstructions. Our method increases context awareness across neighboring windows, leading to a -5.86\% improvement in BD-Rate over existing variable-rate learned image compression techniques. Additionally, our model improves the quality of regions of interest (ROI) in images, making it particularly beneficial for object-focused applications in fields such as manufacturing and industrial vision systems.