论文标题
带有修剪的高位模块的记忆效率学习的图像压缩
Memory-Efficient Learned Image Compression with Pruned Hyperprior Module
论文作者
论文摘要
这些年来，学到的图像压缩（LIC）逐渐变得越来越出名。基于高优先模型的LIC模型已达到了显着的利率延伸性能。但是，这些LIC模型的内存成本太大，无法将它们实际应用于各种设备，尤其是在便携式或边缘设备上。参数刻度直接与内存成本联系在一起。在我们的研究中，我们发现高位模块不仅高度过度参数化，而且其潜在表示包含冗余信息。因此，我们在本文中提出了一种名为ERHP的新型修剪方法，以有效地降低高位模块的内存成本，同时改善网络性能。实验表明我们的方法是有效的，在整个模型中至少降低了22.6％的参数，同时实现了更好的率延伸性能。
Learned Image Compression (LIC) gradually became more and more famous in these years. The hyperprior-module-based LIC models have achieved remarkable rate-distortion performance. However, the memory cost of these LIC models is too large to actually apply them to various devices, especially to portable or edge devices. The parameter scale is directly linked with memory cost. In our research, we found the hyperprior module is not only highly over-parameterized, but also its latent representation contains redundant information. Therefore, we propose a novel pruning method named ERHP in this paper to efficiently reduce the memory cost of hyperprior module, while improving the network performance. The experiments show our method is effective, reducing at least 22.6% parameters in the whole model while achieving better rate-distortion performance.
