PurposeCompression of local specific absorption rate (SAR) matrices is essential for enabling SAR monitoring and efficient pulse calculation in parallel transmission. Improvements in compression result in lower error margin and/or lower number of virtual observation points (VOPs). The purpose of this work is to introduce two algorithms for post‐processing of already compressed VOP sets. One calculates individual overestimation matrices for the VOPs to reduce overestimation, the other identifies redundant VOPs.MethodsThe first algorithm was evaluated for VOP sets calculated for three different transmit arrays with either 8 or 16 channels. For each array, two different overestimation matrices were used to generate the VOP sets. Each post‐processed VOP set was evaluated using one million random excitation vectors and the results compared to the VOP set before post‐processing. The second algorithm was evaluated by utilizing the same random excitation vectors and comparing the results after removal of the redundant VOPs with the results before removal to verify that these were identical.ResultsThe first algorithm reduced the mean overestimation by up to four fifths compared to the original set, while keeping the number of VOPs constant. The second algorithm decreased the number of VOPs generated by a compression with Eichfelder and Gebhardt’s algorithm by more than 40% in 40% of the investigated cases and by more than 20% in 73% of the investigated cases.ConclusionTwo post‐processing algorithms are presented that enhance previously compressed VOP sets by improving the accuracy per number of VOPs.