High levels of noise usually exist in today's captured images due to the relatively small sensors equipped in the smartphone cameras, where the noise brings extra challenges to lossy image compression algorithms. Without the capacity to tell the difference between image details and noise, general image compression methods allocate additional bits to explicitly store the undesired image noise during compression and restore the unpleasant noisy image during decompression. Based on the observations, we optimize the image compression algorithm to be noise-aware as joint denoising and compression to resolve the bits misallocation problem. The key is to transform the original noisy images to noise-free bits by eliminating the undesired noise during compression, where the bits are later decompressed as clean images. Specifically, we propose a novel two-branch, weight-sharing architecture with plug-in feature denoisers to allow a simple and effective realization of the goal with little computational cost. Experimental results show that our method gains a significant improvement over the existing baseline methods on both the synthetic and real-world datasets. Our source code is available at https://github.com/felixcheng97/DenoiseCompression.

翻译：在今天拍摄的图像中,通常会存在高度的噪音,这是因为智能手机相机中安装的传感器相对较小,噪音给丢失图像压缩算法带来额外的挑战。一般图像压缩方法没有能力分辨图像细节和噪音之间的区别,一般图像压缩方法分配了额外的比特点,以便在压缩过程中明确存储不理想的图像噪音,并在压抑过程中恢复令人不快的噪音。根据观察,我们优化图像压缩算法,使图像压缩算法成为有噪音的觉悟,作为联合拆分和压缩来解决比特的错位问题。关键在于通过在压缩过程中消除非理想的噪音,将原始噪音图像转换为无噪音位。在压缩过程中,这些比特点后来被淡化为清洁图像。具体地说,我们建议建立一个新型的双层、重共享结构,配有内嵌入特征的低度缩水器,以便以少量计算成本简单有效地实现目标。实验结果表明,我们的方法在合成和现实世界数据集的现有基线方法上都取得了显著的改进。我们的源代码可以在 https://github.comflischeng97/Droisghegheshion/Dsheshum97。