摘要翻译：
针对图像压缩量化函数不可微的问题，提出了一种通过学习虚拟编解码网络(VCN)实现图像重采样压缩的方法。这里的图像重采样不仅指图像的全分辨率重采样，还包括低分辨率重采样。我们将该方法推广到标准兼容图像压缩(SCIC)框架和基于深度神经网络的图像压缩(DNNC)框架中。具体来说，通过重采样网络(RSN)对输入图像进行测量，得到重采样向量。然后，在SCIC中直接在特征空间对这些向量进行量化，或者对这些向量的离散余弦变换系数进行量化，进一步提高DNNC中的编码效率。在编码器处，量化后的向量或系数通过算术编码进行无损耗压缩。在接收端，图像解码器网络(IDN)利用解码后的向量恢复输入图像。为了以端到端的方式训练RSN网络和IDN网络，我们的VCN网络将重采样向量投影到IDN解码图像。结果表明，从IDN网络到RSN网络的梯度可以近似为VCN网络的梯度。由于在自动编码器结构中，图像重新采样后，可以通过在某个维度空间中的量化进一步实现降维，所以我们可以很好地从预先训练的自动编码器网络初始化我们的网络。通过大量的实验和分析，验证了该方法比现有的许多方法具有更高的有效性和通用性。
---
英文标题：
《Virtual Codec Supervised Re-Sampling Network for Image Compression》
---
作者：
Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao
---
最新提交年份：
2018
---
分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--
一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--

---
英文摘要：
  In this paper, we propose an image re-sampling compression method by learning virtual codec network (VCN) to resolve the non-differentiable problem of quantization function for image compression. Here, the image re-sampling not only refers to image full-resolution re-sampling but also low-resolution re-sampling. We generalize this method for standard-compliant image compression (SCIC) framework and deep neural networks based compression (DNNC) framework. Specifically, an input image is measured by re-sampling network (RSN) network to get re-sampled vectors. Then, these vectors are directly quantized in the feature space in SCIC, or discrete cosine transform coefficients of these vectors are quantized to further improve coding efficiency in DNNC. At the encoder, the quantized vectors or coefficients are losslessly compressed by arithmetic coding. At the receiver, the decoded vectors are utilized to restore input image by image decoder network (IDN). In order to train RSN network and IDN network together in an end-to-end fashion, our VCN network intimates projection from the re-sampled vectors to the IDN-decoded image. As a result, gradients from IDN network to RSN network can be approximated by VCN network's gradient. Because dimension reduction can be further achieved by quantization in some dimensional space after image re-sampling within auto-encoder architecture, we can well initialize our networks from pre-trained auto-encoder networks. Through extensive experiments and analysis, it is verified that the proposed method has more effectiveness and versatility than many state-of-the-art approaches.
---
PDF链接：
https://arxiv.org/pdf/1806.08514