Abstract
Due to their high resolution, pathology images incur substantial storage costs. Although most vendor formats have already adopted lossy compression (typically JPEG) to process such images, the storage requirements remain considerable. However, achieving further reductions in storage requirements while ensuring truly lossless recompression of already lossy-compressed JPEG images remains a critical yet largely unexplored research problem. To address this challenge, we propose a Parallel Hyper-Prior and Dual-Domain Autoregressive Transformer model that processes the three discrete cosine transform (DCT) components in parallel, thereby reducing the complexity of the attention mechanism. The Dual-Domain Autoregressive Transformer module employs a spatial fractal autoregressive transformer, which not only substantially lowers the computational complexity of attention but also captures the inherent hierarchical structure of the spatial domain, and a frequency-domain autoregressive transformer that more effectively models dependencies among coefficients at different frequencies. In addition, we introduce a parameter-sharing and scale-regularized Gaussian-Laplace mixture entropy model to achieve more accurate entropy estimation, thereby enhancing the network's conditional modeling capability and improving compression performance. We validate the proposed method on the public CAMELYON16 dataset (in-distribution) and multiple TCGA datasets (out-of-distribution). Experimental results show that, for lossless JPEG recompression, our approach achieves state-of-the-art performance compared with both traditional and learned JPEG lossless recompression methods, attaining up to 33.4% additional lossless compression on standard JPEG files. Moreover, the method maintains strong generalization to out-of-distribution data. The source code has been made publicly available at: https://github.com/zhuiguangzhe123/PLR.