论文标题
感知优化深层图像压缩
Perceptually Optimizing Deep Image Compression
论文作者
论文摘要
平均平方误差（MSE）和$ \ ell_p $规范由于其简单性和分析性能而在很大程度上主导了神经网络损失的测量。但是，当用来评估视觉信息丢失时，这些简单的规范与人类的感知不一致。在这里，我们提出了一种不同的代理方法，以优化针对定量感知模型的图像分析网络。具体而言，我们构建了一个代理网络，该网络在作为网络的损耗层时模仿感知模型。我们在实验上证明了如何应用此优化框架来训练端到端的优化优化图像压缩网络。通过在现代深层图像压缩模型之上构建，我们能够证明比MSE优化的平均比特率降低了$ 28.7 \％$ $，鉴于指定的感知质量（VMAF）水平。
Mean squared error (MSE) and $\ell_p$ norms have largely dominated the measurement of loss in neural networks due to their simplicity and analytical properties. However, when used to assess visual information loss, these simple norms are not highly consistent with human perception. Here, we propose a different proxy approach to optimize image analysis networks against quantitative perceptual models. Specifically, we construct a proxy network, which mimics the perceptual model while serving as a loss layer of the network.We experimentally demonstrate how this optimization framework can be applied to train an end-to-end optimized image compression network. By building on top of a modern deep image compression models, we are able to demonstrate an averaged bitrate reduction of $28.7\%$ over MSE optimization, given a specified perceptual quality (VMAF) level.
