Abstract: Artificial intelligence applications require highly advanced high-speed video imaging technologies to perceive the surrounding environment better. Deep learning-based snapshot compressive imaging (SCI) offers a promising solution. How to reconstruct high-speed videos from observed data using deep learning techniques is a frontier hotspot in the field. However, existing reconstruction methods focus on mining prior information, neglecting the direct influence of masks and image textures on reconstruction difficulty. There is still room for further improvement in reconstruction quality. To address this issue, we propose a reconstruction difficulty perception-based SCI (RdpSCI) method. Based on the observation that masks and image textures jointly determine the information contained in the observed data, RdpSCI first proposes to explore the correlation between masks, image textures, and reconstruction difficulty, guiding deep networks for reconstruction. Specifically, it introduces an improved residual dense network (I-ResDNet) module, innovatively incorporating channel shuffling operations into ResDNet to reduce the dependency of feature fusion effects on channel partitioning methods. The proposed I-ResDNet also introduces a reconstruction difficulty weight vector to guide feature fusion, enhancing feature fusion capability without significantly increasing model parameters. Experiments show that compared to the state-of-the-art methods STFormer and EfficientSCI, RdpSCI achieves improvements of 0.68 dB and 0.54 dB in reconstruction quality on benchmark grayscale and colour datasets, respectively.