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：摘要：渐进式压缩技术允许图像以低分辨率版本开始加载，随着接收更多数据而逐渐清晰。这在网络连接较慢等场景下可提升用户体验。目前，大多数经典与学习型图像压缩方法均被设计为非渐进式。本文提出ProgDTD训练方法，可将学习型非渐进式图像压缩方法转化为渐进式方法。ProgDTD的设计基于如下观察：压缩模型瓶颈层中存储的信息重要性通常存在差异。为实现渐进式压缩模型，ProgDTD通过修改训练步骤强制模型按优先级排序的方式在瓶颈层中存储数据。我们通过按排序索引顺序传输数据来实现渐进式压缩。ProgDTD专为基于CNN的学习型图像压缩模型设计，无需额外参数，且支持自定义渐进程度范围。为进行评估，我们将ProgDTD应用于超先验模型（学习型图像压缩中最常见的结构之一）。实验结果表明，在MS-SSIM及精确度指标上，ProgDTD与非渐进式对应方法及其他先进渐进式模型的性能相当。