Trit-plane coding enables deep progressive image compression, but it cannot use autoregressive context models. In this paper, we propose the context-based trit-plane coding (CTC) algorithm to achieve progressive compression more compactly. First, we develop the context-based rate reduction module to estimate trit probabilities of latent elements accurately and thus encode the trit-planes compactly. Second, we develop the context-based distortion reduction module to refine partial latent tensors from the trit-planes and improve the reconstructed image quality. Third, we propose a retraining scheme for the decoder to attain better rate-distortion tradeoffs. Extensive experiments show that CTC outperforms the baseline trit-plane codec significantly in BD-rate on the Kodak lossless dataset, while increasing the time complexity only marginally. Our codes are available at https://github.com/seungminjeon-github/CTC.

翻译：Trit 平面编码实现了深度渐进式图像压缩，但无法使用自回归上下文模型。本文提出基于上下文的 Trit 平面编码（CTC）算法，以实现更紧凑的渐进压缩。首先，我们开发了基于上下文的码率缩减模块，用于精确估计潜在元素的 Trit 概率，从而紧凑地编码 Trit 平面。其次，我们开发了基于上下文的失真缩减模块，用于精炼来自 Trit 平面的部分潜在张量，并提升重建图像质量。第三，我们提出一种解码器重训练方案，以获得更优的率失真权衡。大量实验表明，在 Kodak 无损数据集上，CTC 在 BD-rate 指标上显著优于基准 Trit 平面编解码器，同时仅略微增加时间复杂度。我们的代码可在 https://github.com/seungminjeon-github/CTC 获取。