Video compressed sensing reconstruction based on structural group sparsity and successive approximation estimation model

•To balance reconstruction quality with computational complexity, we introduce a structural group sparsity model for use in the initial reconstruction phase and propose a weight-based group sparse optimization algorithm acting in joint domains.•Then, a coarse-to-fine optical flow estimation model with successive approximation is introduced for use in the interframe prediction stage to recover non-key frames through alternating optical flow estimation and residual sparse reconstruction.•The experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in both objective and subjective reconstruction quality.