为表情识别优化的图像压缩模型，兼顾清晰度与识别率。
Optimized Learned Image Compression for Facial Expression Recognition
- 设计端到端模型，用定制损失函数平衡压缩与识别性能。
- 联合优化使识别准确率提升4.04%，压缩效率提高89.12%。
- 适合需要高压缩比仍保持高识别精度的应用场景。
高效的数据压缩对视觉数据的存储和传输至关重要。然而，在面部表情识别（FER）任务中，有损压缩常导致特征退化和准确率下降。为此，本文提出一种端到端模型，旨在保留关键特征并同时提升压缩与识别性能。引入定制损失函数以有效平衡压缩与识别表现，并研究不同损失权重对平衡的影响。实验表明，仅微调压缩模型可使分类准确率提升0.71%，压缩效率提高49.32%；而联合优化则实现准确率提升4.04%、压缩效率提升89.12%。此外，联合优化后的分类模型在压缩与未压缩数据上均保持高准确率，且压缩模型在高压缩率下仍能可靠保留图像细节。
原文摘要 · Abstract (English)
Efficient data compression is crucial for the storage and transmission of visual data. However, in facial expression recognition (FER) tasks, lossy compression often leads to feature degradation and reduced accuracy. To address these challenges, this study proposes an end-to-end model designed to preserve critical features and enhance both compression and recognition performance. A custom loss function is introduced to optimize the model, tailored to balance compression and recognition performance effectively. This study also examines the influence of varying loss term weights on this balance. Experimental results indicate that fine-tuning the compression model alone improves classification accuracy by 0.71% and compression efficiency by 49.32%, while joint optimization achieves significant gains of 4.04% in accuracy and 89.12% in efficiency. Moreover, the findings demonstrate that the jointly optimized classification model maintains high accuracy on both compressed and uncompressed data, while the compression model reliably preserves image details, even at high compression rates.
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