《An End-to-End Compression Framework Based on Convolutional Neural Networks》笔记 - 代码天地

《An End-to-End Compression Framework Based on Convolutional Neural Networks》笔记

其他 2019-11-28 20:17:27 阅读次数: 0

论文阅读笔记

《An End-to-End Compression Framework Based on Convolutional Neural Networks》

基本信息

时间：2018年

刊物： IEEE Transactions on Circuits and Systems for Video Technology(TCSVT)

作者：哈工大团队

关键词

卷积神经网络 CNN；紧凑表示；压缩框架

主要思路

CNN+传统编解码器+CNN，第一个CNN负责数据的紧凑表示，第二个CNN负责重建图像
编解码器保留传统思路，变换编码+量化+熵编码
设计了一个优化学习算法，解决量化过程中不可微的问题，从而可以用反向传播整体训练模型

内容

第一个CNN有三层，(1) conv+ReLU (2) conv+BN(Batch Normalization)+ReLU (3)Conv
第二个CNN有20层，和第一个CNN结构差不多，只是2~19层都是 (2) 部分
解码后的数据首先经过一个双三次插值向上采样，然后经过第二个CNN重建图像，最后将上采样结果与CNN网络输出的残差图像结合得到复原图像。

创新点

整体训练，解决了量化过程不可微分的问题

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