This paper provides a comprehensive study on features and performance of different ways to incorporate neural networks into lifting-based wavelet-like transforms, within the context of fully scalable and accessible image compression. Specifically, we explore different arrangements of lifting steps, as well as various network architectures for learned lifting operators. Moreover, we examine the impact of the number of learned lifting steps, the number of channels, the number of layers and the support of kernels in each learned lifting operator. To facilitate the study, we investigate two generic training methodologies that are simultaneously appropriate to a wide variety of lifting structures considered. Experimental results ultimately suggest that retaining fixed lifting steps from the base wavelet transform is highly beneficial. Moreover, we demonstrate that employing more learned lifting steps and more layers in each learned lifting operator do not contribute strongly to the compression performance. However, benefits can be obtained by utilizing more channels in each learned lifting operator. Ultimately, the learned wavelet-like transform proposed in this paper achieves over 25% bit-rate savings compared to JPEG 2000 with compact spatial support.

翻译：本文对在完全可扩展与可访问的图像压缩背景下，将神经网络融入基于提升的小波类变换的不同方式进行了全面研究，涵盖其特征与性能。具体而言，我们探索了提升步骤的不同排列方式，以及用于学习型提升算子的多种网络架构。此外，我们考察了学习型提升步骤数量、各学习型提升算子中的通道数、层数以及核支撑范围的影响。为便于研究，我们研究了两种通用的训练方法，这些方法同时适用于所考虑的各种提升结构。实验结果最终表明，保留基础小波变换中的固定提升步骤极为有益。此外，我们证明，在每个学习型提升算子中使用更多的学习型提升步骤和更多层并不能显著提升压缩性能。然而，在每个学习型提升算子中利用更多通道可带来性能提升。最终，本文提出的学习型小波类变换相较于具有紧凑空间支撑的JPEG 2000，实现了超过25%的比特率节省。