Ultra high resolution (UHR) images are almost always downsampled to fit small displays of mobile end devices and upsampled to its original resolution when exhibited on very high-resolution displays. This observation motivates us on jointly optimizing operation pairs of downsampling and upsampling that are spatially adaptive to image contents for maximal rate-distortion performance. In this paper, we propose an adaptive downsampled dual-layer (ADDL) image compression system. In the ADDL compression system, an image is reduced in resolution by learned content-adaptive downsampling kernels and compressed to form a coded base layer. For decompression the base layer is decoded and upconverted to the original resolution using a deep upsampling neural network, aided by the prior knowledge of the learned adaptive downsampling kernels. We restrict the downsampling kernels to the form of Gabor filters in order to reduce the complexity of filter optimization and also reduce the amount of side information needed by the decoder for adaptive upsampling. Extensive experiments demonstrate that the proposed ADDL compression approach of jointly optimized, spatially adaptive downsampling and upconversion outperforms the state of the art image compression methods.

翻译：超高清（UHR）图像几乎总是被下采样以适应移动终端设备的小型显示器，并在超高分辨率显示器上展示时上采样至原始分辨率。这一观察启发我们联合优化下采样与上采样操作对，使其在空间上适应图像内容，从而实现最优的率失真性能。本文提出一种自适应下采样双层（ADDL）图像压缩系统。在ADDL压缩系统中，通过学习的自适应下采样核降低图像分辨率，并将其压缩编码为基底层。解码时，基底层被解码，并借助已学习的自适应下采样核的先验知识，利用深度上采样神经网络将其上转换至原始分辨率。我们将下采样核限制为加伯滤波器形式，以降低滤波器优化的复杂度，并减少解码器进行自适应上采样所需的辅助信息量。大量实验表明，所提出的联合优化、空间自适应的下采样与上转换ADDL压缩方法，性能优于当前最先进的图像压缩方法。