Abstract
Deep learning has revolutionized many computer vision fields in the last few years, including learning-based image compression. In this paper, we propose a deep semantic segmentation-based layered image compression (DSSLIC) framework in which the semantic segmentation map of the input image is obtained and encoded as the base layer of the bit-stream. A compact representation of the input image is also generated and encoded as the first enhancement layer. The segmentation map and the compact version of the image are then employed to obtain a coarse reconstruction of the image. The residual between the input and the coarse reconstruction is additionally encoded as another enhancement layer. Experimental results show that the proposed framework outperforms the H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics across a wide range of bit rates in RGB domain. Besides, since semantic segmentation map is included in the bit-stream, the proposed scheme can facilitate many other tasks such as image search and object-based adaptive image compression.
Abstract (translated)
深度学习在过去几年中彻底改变了许多计算机视觉领域，包括基于学习的图像压缩。在本文中，我们提出了一种基于深度语义分割的分层图像压缩（DSSLIC）框架，其中获得输入图像的语义分割图并将其编码为比特流的基础层。还生成输入图像的紧凑表示并将其编码为第一增强层。然后采用分割图和图像的紧凑版本来获得图像的粗略重建。输入和粗略重建之间的残差另外被编码为另一增强层。实验结果表明，所提出的框架在RGB域中的各种比特率上优于基于H.265 / HEVC的BPG和PSNR和MS-SSIM度量中的其他编解码器。此外，由于语义分割图包含在比特流中，所提出的方案可以促进许多其他任务，例如图像搜索和基于对象的自适应图像压缩。
URL
https://arxiv.org/abs/1806.03348
