The accuracy, resolution, and economic cost of near-field acoustic holography (NAH) are highly dependent on the number of spatial sampling points. Generally, higher accuracy and resolution require more spatial sampling points, which may increase the workload of measurement or the hardware cost. Compressive sensing (CS) is able to solve the underdetermined problems by utilizing the sparsity of signals, and thus it can be applied to NAH to reduce the number of spatial sampling points but at the same time provide a high-resolution reconstruction image. Based on the CS theory, this paper proposes a compressed modal equivalent point source method (CMESM). In the method, a sparse basis that is obtained from the eigen-decomposition of the power resistance matrix is introduced to compress the equivalent point source strengths, and the ℓ1–norm minimization is used to promote sparse solutions. Both numerical simulation and experimental results demonstrate the validity of the proposed CMESM and show its advantage over the existing methods when the number of spatial sampling points is reduced.