gSlider is a diffusion MRI method that achieves fast high-resolution data acquisition using a novel slab-selective RF-encoding strategy. Recent work has proposed subsampling of the multidimensional gSlider encoding space (diffusion-encoding/RF-encoding) for further improved scan efficiency. Two different q-space regularization approaches (i.e., Laplace-Beltrami smoothness and spherical ridgelet sparsity) have been proposed to compensate for missing data, but there have been no systematic comparisons between the two. We compare and evaluate the potential synergies of these regularization approaches. Results suggest that there can be small advantages to combining both regularization strategies together, although Laplace-Beltrami regularization alone is simpler and not much worse.