High-resolution images are widely used in our everyday life; however, high-speed video capture is more challenging due to the low frame rate of cameras working at the high-resolution mode. The main bottleneck lies in the low throughput of existing imaging systems. Toward this end, snapshot compressive imaging (SCI) was proposed as a promising solution to improve the throughput of imaging systems by compressive sampling and computational reconstruction. During acquisition, multiple high-speed images are encoded and collapsed to a single measurement. Then, algorithms are employed to retrieve the video frames from the coded snapshot. Recently developed plug-and-play algorithms made the SCI reconstruction possible in large-scale problems. However, the lack of high-resolution encoding systems still precludes SCI’s wide application. Thus, in this paper, we build, to the best of our knowledge, a novel hybrid coded aperture snapshot compressive imaging (HCA-SCI) system by incorporating a dynamic liquid crystal on silicon and a high-resolution lithography mask. We further implement a PnP reconstruction algorithm with cascaded denoisers for high-quality reconstruction. Based on the proposed HCA-SCI system and algorithm, we obtain a 10-mega-pixel SCI system to capture high-speed scenes, leading to a high throughput of 4.6 × 10⁹ voxels per second. Both simulation and real-data experiments verify the feasibility and performance of our proposed HCA-SCI scheme.