用一个额外参数提升图像压缩概率模型灵活性。
Generalized Gaussian Model for Learned Image Compression
- 在高斯模型基础上引入形状参数，构建更灵活的广义高斯模型。
- 训练时通过参数依赖下界和梯度修正，减少训练测试差异。
- 在多个压缩网络中优于传统高斯与混合高斯模型。
在学习型图像压缩中，概率模型对表征潜在变量分布至关重要。以均值和尺度参数为基础的高斯模型因简洁有效而被广泛使用。具有更多参数的概率模型（如高斯混合模型）可更精确拟合潜在变量分布，但复杂度更高。为平衡压缩性能与复杂度，本文将高斯模型扩展至广义高斯族，仅增加一个额外形状参数beta，实现更灵活的潜在分布建模。为缓解训练-测试不匹配问题，提出改进训练方法，包括尺度参数的beta相关下界和梯度修正。所提广义高斯模型结合优化训练策略，在多种学习型图像压缩网络上均优于高斯模型和高斯混合模型。
原文摘要 · Abstract (English)
In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters has been widely used for its simplicity and effectiveness. Probabilistic models with more parameters, such as the Gaussian mixture models, can fit the distribution of latent variables more precisely, but the corresponding complexity is higher. To balance the compression performance and complexity, we extend the Gaussian model to the generalized Gaussian family for more flexible latent distribution modeling, introducing only one additional shape parameter beta than the Gaussian model. To enhance the performance of the generalized Gaussian model by alleviating the train-test mismatch, we propose improved training methods, including beta-dependent lower bounds for scale parameters and gradient rectification. Our proposed generalized Gaussian model, coupled with the improved training methods, is demonstrated to outperform the Gaussian and Gaussian mixture models on a variety of learned image compression networks.
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