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In this correspondence, we develop techniques to efficiently quantize channel covariance matrices in multiple-input multiple-output (MIMO) Rayleigh fading environments. While these covariance matrices change less frequently than the channel matrices themselves, this information needs to be updated when it does change. Furthermore, these covariance matrices have significantly more parameters to quantize than their channel matrix counterparts. Since many applications focus on utilizing the strongest eigenmodes of the channel covariance matrix and since these matrices tend to be low-rank, we focus on efficiently quantizing the dominant eigenvectors of these matrices. We develop Lloyd-type algorithms based on training data from the environment to develop our codebooks. We also develop an algorithm based on the reduced-parameter Kronecker and Weichselberger models to generate codebooks with reduced real-time codeword search.