Progressive compression allows images to start loading as low-resolution versions, becoming clearer as more data is received. This increases user experience when, for example, network connections are slow. Today, most approaches for image compression, both classical and learned ones, are designed to be non-progressive. This paper introduces ProgDTD, a training method that transforms learned, non-progressive image compression approaches into progressive ones. The design of ProgDTD is based on the observation that the information stored within the bottleneck of a compression model commonly varies in importance. To create a progressive compression model, ProgDTD modifies the training steps to enforce the model to store the data in the bottleneck sorted by priority. We achieve progressive compression by transmitting the data in order of its sorted index. ProgDTD is designed for CNN-based learned image compression models, does not need additional parameters, and has a customizable range of progressiveness. For evaluation, we apply ProgDTDto the hyperprior model, one of the most common structures in learned image compression. Our experimental results show that ProgDTD performs comparably to its non-progressive counterparts and other state-of-the-art progressive models in terms of MS-SSIM and accuracy.

翻译：渐进式压缩技术允许图像以低分辨率版本开始加载，并在接收更多数据后逐渐变得清晰。当网络连接速度较慢时，这能显著提升用户体验。当前大部分图像压缩方法（无论是传统方法还是学习方法）都设计为非渐进式的。本文提出ProgDTD训练方法，可将非渐进式的学习型图像压缩方法转化为渐进式方法。ProgDTD的设计基于以下发现：压缩模型瓶颈层中存储的信息其重要性通常存在差异。为构建渐进式压缩模型，ProgDTD修改训练步骤，强制模型按优先级顺序存储瓶颈层数据。我们通过按排序索引顺序传输数据实现渐进式压缩。ProgDTD专为基于CNN的学习型图像压缩模型设计，无需额外参数，且具有可自定义的渐进范围。为进行评估，我们将ProgDTD应用于学习型图像压缩中最常见的结构之一——超先验模型。实验结果表明，在MS-SSIM和准确率指标上，ProgDTD的性能与其非渐进式对应方法及其他先进渐进式模型相当。