Abstract
In this paper, we propose an end-to-end mixed-resolution image compression framework with convolutional neural networks. Firstly, given one input image, feature description neural network (FDNN) is used to generate a new representation of this image, so that this image representation can be more efficiently compressed by standard codec, as compared to the input image. Furthermore, we use post-processing neural network (PPNN) to remove the coding artifacts caused by quantization of codec. Secondly, low-resolution image representation is adopted for high efficiency compression in terms of most of bit spent by image's structures under low bit-rate. However, more bits should be assigned to image details in the high-resolution, when most of structures have been kept after compression at the high bit-rate. This comes from a fact that the low-resolution image representation can't burden more information than high-resolution representation beyond a certain bit-rate. Finally, to resolve the problem of error back-propagation from the PPNN network to the FDNN network, we introduce to learn a virtual codec neural network to imitate two continuous procedures of standard compression and post-processing. The objective experimental results have demonstrated the proposed method has a large margin improvement, when comparing with several state-of-the-art approaches.
Abstract (translated)
在本文中，我们提出了一种具有卷积神经网络的端到端混合分辨率图像压缩框架。首先，给定一个输入图像，特征描述神经网络（FDNN）用于生成该图像的新表示，使得与输入图像相比，可以通过标准编解码器更有效地压缩该图像表示。此外，我们使用后处理神经网络（PPNN）来消除由编解码器的量化引起的编码伪像。其次，就低比特率下图像结构花费的大部分比特而言，采用低分辨率图像表示进行高效压缩。但是，当在高比特率压缩之后保留大多数结构时，应该以高分辨率为图像细节分配更多比特。这是因为低分辨率图像表示不能比超过特定比特率的高分辨率表示负担更多信息。最后，为了解决从PPNN网络到FDNN网络的错误反向传播问题，我们介绍了学习虚拟编解码器神经网络来模拟标准压缩和后处理的两个连续过程。客观实验结果表明，与几种最先进的方法相比，所提出的方法有很大的改进。
URL
https://arxiv.org/abs/1802.01447
