All our variant calling algorithms rely heavily on the quality scores assigned to the individual base calls in each sequence read. These scores are per-base estimates of error emitted by the sequencing machines. Unfortunately the scores produced by the machines are subject to various sources of systematic error, leading to over- or under-estimated base quality scores in the data. Base quality score recalibration is a process in which we apply machine learning to model these errors empirically and adjust the quality scores accordingly. This allows us to get more accurate base qualities, which in turn improves the accuracy of our variant calls. The base recalibration process involves two key steps: first the program builds a model of covariation based on the data and a set of known variants (which you can bootstrap if there is none available for your organism), then it adjusts the base quality scores in the data based on the model.
In addition, there is an optional but highly recommended step that involves building a second model and generating before/after plots to visualize the effects of the recalibration process.