Lossless compression algorithm for tongue images based on cross-channel residual predictive coding

Tongue images are increasingly employed as diagnostic data in intelligent clinical systems. However, the storage and bandwidth costs of high-resolution image data can reduce diagnostic efficiency, making image compression a critical tool for balancing data fidelity, transmission cost, and retrieval latency. General-purpose methods fail to fully exploit the cross-channel correlations and smooth textures of tongue images, leading this study to propose a lossless compression algorithm based on a residual prediction framework. The compression pipeline comprises four stages: First, integer lifting performs reversible color transformation, eliminating interchannel redundancy. Second, an enhanced LOCO-I model incorporating gradient detection and “Left-fused” mode captures smooth tongue textures. Third, an S-shaped scan reorders residuals to preserve spatial locality. Finally, cross-channel differential type mapping, dynamic bit-width packing, and hybrid entropy coding mitigate statistical redundancy. Experimental results across three tongue datasets demonstrate that the proposed algorithm exceeds the compression ratios of standard methods by 6%, 9% and 1%, respectively. It significantly outperforms standard PNG and JPEG 2000 formats while maintaining a decoding latency comparable to QOI. Furthermore, generalization tests on endoscopic images of colonic polyps, retinal fundus images, and dermatological images show compression improvements of 7%, 5% and 6%, respectively. The proposed method achieves a robust balance between high compression efficiency and low-latency decoding while ensuring faithful reconstruction within practical engineering constraints.