T1ρ mapping requires several T1ρ-weighted images with different spin lock times to obtain the T1ρ maps, resulting in a long scan time.Compressed sensing has shown good performance in fast quantitative T1ρ mapping. In this work, we developed a variable acceleration rates undersampling strategy to reduce the scan time. A signal compensation with low-rank plus sparse model was used to reconstruct the T1ρ-weighted images. Specifically, a feature descriptor was used to pick up useful features from the residual images. Preliminary results show that the proposed method achieves a 5.76-fold acceleration and obtain more accurate T1ρ maps than the existing methods.