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：三值平面编码能够实现深度渐进式图像压缩，但无法利用自回归上下文模型。本文提出基于上下文的二值平面编码（CTC）算法，以更紧凑的方式实现渐进压缩。首先，我们开发了基于上下文的码率缩减模块，准确估计潜在元素的三值概率，从而紧凑地编码三值平面。其次，我们开发了基于上下文的失真缩减模块，对来自三值平面的部分潜在张量进行细化，提升重建图像质量。第三，我们提出了一种解码器重训练方案，以取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指标上显著优于基线三值平面编解码器，同时仅带来微小的时间复杂度增加。我们的代码开源在https://github.com/seungminjeon-github/CTC。