We propose a versatile deep image compression network based on Spatial
Feature Transform (SFT arXiv:1804.02815), which takes a source image and a
corresponding quality map as inputs and produce a compressed image with
variable rates. Our model covers a wide range of compression rates using a
single model, which is controlled by arbitrary pixel-wise quality maps. In
addition, the proposed framework allows us to perform task-aware image
compressions for various tasks, e.g., classification, by efficiently estimating
optimized quality maps specific to target tasks for our encoding network. This
is even possible with a pretrained network without learning separate models for
individual tasks. Our algorithm achieves outstanding rate-distortion trade-off
compared to the approaches based on multiple models that are optimized
separately for several different target rates. At the same level of
compression, the proposed approach successfully improves performance on image
classification and text region quality preservation via task-aware quality map
estimation without additional model training. The code is available at the
project website: this https URL

提出了一种基于空间特征变换的通用深度图像压缩网络，可以使用单个模型进行广泛的压缩率，允许任务感知图像压缩并通过优化的质量地图实现任务特定的质量要求。

通过空间自适应特征变换实现变速深度图像压缩

Variable-Rate Deep Image Compression through Spatially-Adaptive Feature  Transform

Despite that convolutional neural networks (CNN) have recently demonstrated
high-quality reconstruction for single-image super-resolution (SR), recovering
natural and realistic texture remains a challenging problem. In this paper, we
show that it is possible to recover textures faithful to semantic classes. In
particular, we only need to modulate features of a few intermediate layers in a
single network conditioned on semantic segmentation probability maps. This is
made possible through a novel Spatial Feature Transform (SFT) layer that
generates affine transformation parameters for spatial-wise feature modulation.
SFT layers can be trained end-to-end together with the SR network using the
same loss function. During testing, it accepts an input image of arbitrary size
and generates a high-resolution image with just a single forward pass
conditioned on the categorical priors. Our final results show that an SR
network equipped with SFT can generate more realistic and visually pleasing
textures in comparison to state-of-the-art SRGAN and EnhanceNet.

本文提出一种基于语义分割概率图的空间特征变换（SFT）层，能有效恢复语义类匹配的纹理特征，使得卷积神经网络（CNN）进行单图像超分辨率（SR）重建更具真实性和视觉感受性。