论文标题
学习用于绿色AI图像编码的稀疏自动编码器
Learning sparse auto-encoders for green AI image coding
论文作者
论文摘要
最近，引入了卷积自动编码器（CAE）进行图像编码。他们对最先进的JPEG2000方法实现了绩效改进。但是，这些表演是使用大量参数的大量CAE获得的，并且其训练需要大量的计算能力。\\在本文中，我们使用具有较小的内存足迹和低计算功率使用的CAE解决了有损图像压缩的问题。为了克服计算成本问题，大多数文献都使用拉格朗日近端正规化方法，这些方法耗时。\\在这项工作中，我们提出了一种约束的方法和一种新的结构化稀疏学习方法。我们设计了算法并对三个约束进行测试：经典$ \ ell_1 $约束，$ \ ell_ {1，\ infty} $和新的$ \ ell_ {1,1} $约束。实验结果表明，$ \ ell_ {1,1} $约束提供了最佳的结构性稀疏性，从而高度降低了内存和计算成本，并且与密集的网络具有相似的速率延伸性能。
Recently, convolutional auto-encoders (CAE) were introduced for image coding. They achieved performance improvements over the state-of-the-art JPEG2000 method. However, these performances were obtained using massive CAEs featuring a large number of parameters and whose training required heavy computational power.\\ In this paper, we address the problem of lossy image compression using a CAE with a small memory footprint and low computational power usage. In order to overcome the computational cost issue, the majority of the literature uses Lagrangian proximal regularization methods, which are time consuming themselves.\\ In this work, we propose a constrained approach and a new structured sparse learning method. We design an algorithm and test it on three constraints: the classical $\ell_1$ constraint, the $\ell_{1,\infty}$ and the new $\ell_{1,1}$ constraint. Experimental results show that the $\ell_{1,1}$ constraint provides the best structured sparsity, resulting in a high reduction of memory and computational cost, with similar rate-distortion performance as with dense networks.
