Recently deep learning has been introduced to the field of image compression. In this paper, we present a hybrid coding framework that combines entropy coding, deep learning, and traditional coding framework. In the base layer of the encoding, we use convolutional neural networks to learn the latent representation and importance map of the original image respectively. The importance map is then used to guide the bit allocation of the latent representation. A context model is also developed to help the entropy coding after the masked quantization. Another network is used to get a coarse reconstruction of the image in the base layer. The residual between the input and the coarse reconstruction is then obtained and encoded by the traditional BPG codec as the enhancement layer of the bit stream. We only need to train a basic model and the proposed scheme can realize image compression at different bit rates, thanks to the use of the traditional codec. Experimental results using the Kodak, Urban100 and BSD100 datasets show that the proposed scheme outperforms many deep learning-based methods and traditional codecs including BPG in MS-SSIM metric across a wide range of bit rates. It also exceeds some latest hybrid schemes in RGB444 domain on Kodak dataset in both PSNR and MS-SSIM metrics.

最近，深度学习已被引入到图像压缩领域。在本文中，我们提出了一种混合编码框架，该框架结合了熵编码，深度学习和传统编码框架。在编码的基础层，我们使用卷积神经网络分别学习原始图像的潜在表示和重要性图。然后，重要性图用于指导潜在表示的位分配。还开发了上下文模型来帮助掩盖量化后进行熵编码。另一个网络用于对基础层中的图像进行粗略重建。然后，获得输入和粗略重构之间的残差，并通过传统的BPG编解码器将其编码为位流的增强层。我们只需要训练一个基本模型，由于使用了传统的编解码器，因此所提出的方案可以在不同的比特率下实现图像压缩。使用Kodak，Urban100和BSD100数据集的实验结果表明，所提出的方案在许多比特率上均优于许多基于深度学习的方法和传统编解码器，包括MS-SSIM度量中的BPG。在PSNR和MS-SSIM指标上，它还超过了柯达数据集RGB444域中的一些最新混合方案。