Variable Rate Image Compression via N-Gram Context based Swin-transformer

Priyanka Mudgal

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arXiv · 2510.00058

Variable Rate Image Compression via N-Gram Context based Swin-transformer

Priyanka Mudgal

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Abstract

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.

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eess.IV cs.CV cs.MM

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Priyanka Mudgal. 2025-09-28. Variable Rate Image Compression via N-Gram Context based Swin-transformer. https://arxiv.org/abs/2510.00058

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