Image Signal Processors (ISPs) play important roles in image recognition tasks as well as in the perceptual quality of captured images. In most cases, experts make a lot of effort to manually tune many parameters of ISPs, but the parameters are sub-optimal. In the literature, two types of techniques have been actively studied: a machine learning-based parameter tuning technique and a DNN-based ISP technique. The former is lightweight but lacks expressive power. The latter has expressive power, but the computational cost is too heavy on edge devices. To solve these problems, we propose "DynamicISP," which consists of multiple classical ISP functions and dynamically controls the parameters of each frame according to the recognition result of the previous frame. We show our method successfully controls the parameters of multiple ISP functions and achieves state-of-the-art accuracy with low computational cost in single and multi-category object detection tasks.

翻译：图像信号处理器（ISP）在图像识别任务以及抓拍图像的感知质量方面起着重要作用。在大多数情况下，专家们会花费大量力气手动调节ISP的许多参数，但这些参数是次优的。在文献中，已经积极研究了两种类型的技术：一种是基于机器学习的参数调整技术，另一种则是基于深度学习网络的ISP技术。前者虽然轻量级但缺乏表现力；后者虽然表现力强，但在边缘设备上的计算成本太高。为了解决这些问题，我们提出了“DynamicISP”，它由多个经典ISP功能组成，并根据上一帧的识别结果动态控制每一帧的参数。我们展示了我们的方法成功地控制了多个ISP功能的参数，并在单分类和多分类目标检测任务中以低计算成本实现了最新的精度。