For camera-based image capturing, the impact of exposure or camera parameters (ISO sensitivity, shutter speed, and aperture F-number) on imaging quality is decisive. Such parameters interact in a coupled manner during the imaging process to determine the exposure quality and the degree of blur in a photograph. Naturally, decoupling such parameters from images holds significant value for applications like image quality assessment and illumination optimization. However, there has been no systematic research dedicated to this topic. In this paper, we propose a new benchmark, Cam-Bench, for estimating camera parameters on images directly. It collects an image dataset Cam-10K with various indoor scenes and accurate labels of camera parameters. Based on Cam-10K, we propose a camera parameter estimation network to decouple and regress recorded exposure information. To the best of our knowledge, Cam-Bench is the first benchmark for camera parameter estimation. Experiments demonstrate that it can enhance the performance of various downstream applications.The source code has been made publicly available at: https://github.com/pengquanhong/CamBench.