论文标题
RAWTOBIT：完全端到端的相机ISP网络
RAWtoBit: A Fully End-to-end Camera ISP Network
论文作者
论文摘要
图像压缩是相机图像信号处理（ISP）管道中必不可少的最后处理单元。虽然已经进行了许多研究，以替换传统的ISP管道使用单个端到端优化的深度学习模型，但图像压缩几乎不被视为模型的一部分。在本文中，我们研究了完全端到端优化的相机ISP的设计，该相机融合了图像压缩。为此，我们提出了可以同时执行这两个任务的RAWTOBIT网络（RBN）。通过引入两个专门从事每个任务的教师网络，通过新颖的知识蒸馏计划进一步改善了RBN。广泛的实验表明，我们所提出的方法在利率折衷方面显着优于替代方法。
Image compression is an essential and last processing unit in the camera image signal processing (ISP) pipeline. While many studies have been made to replace the conventional ISP pipeline with a single end-to-end optimized deep learning model, image compression is barely considered as a part of the model. In this paper, we investigate the designing of a fully end-to-end optimized camera ISP incorporating image compression. To this end, we propose RAWtoBit network (RBN) that can effectively perform both tasks simultaneously. RBN is further improved with a novel knowledge distillation scheme by introducing two teacher networks specialized in each task. Extensive experiments demonstrate that our proposed method significantly outperforms alternative approaches in terms of rate-distortion trade-off.
