论文标题
基于深度学习的图像压缩和格子编码的量化
Deep Learning-based Image Compression with Trellis Coded Quantization
论文作者
论文摘要
最近，许多工作试图基于深度学习体系结构开发图像压缩模型，其中均匀的标量量化器（SQ）通常应用于编码器和解码器之间的特征图。在本文中，我们建议将格子编码的量化器（TCQ）纳入基于深度学习的图像压缩框架中。采用软策略，以允许在训练期间进行后退传播。我们开发了一个简单的图像压缩模型，该模型由三个子网（编码器，解码器和熵估计）组成，并以端到端方式优化所有组件。我们在两个高分辨率图像数据集上进行了实验，并且都表明我们的模型可以在低比特速率下实现出色的性能。我们还根据我们提出的基线模型来显示TCQ和SQ之间的比较，并证明了TCQ的优势。
Recently many works attempt to develop image compression models based on deep learning architectures, where the uniform scalar quantizer (SQ) is commonly applied to the feature maps between the encoder and decoder. In this paper, we propose to incorporate trellis coded quantizer (TCQ) into a deep learning based image compression framework. A soft-to-hard strategy is applied to allow for back propagation during training. We develop a simple image compression model that consists of three subnetworks (encoder, decoder and entropy estimation), and optimize all of the components in an end-to-end manner. We experiment on two high resolution image datasets and both show that our model can achieve superior performance at low bit rates. We also show the comparisons between TCQ and SQ based on our proposed baseline model and demonstrate the advantage of TCQ.
