1. A method for compression of an N×N covariance matrix, wherein N is a positive integer less than four, comprising: decomposing said N×N covariance matrix into N eigenvalues and N eigenvectors in a first computer process; compressing each eigenvalue in a second computer process; calculating N Euler angles that represent the orientation of the N eigenvectors in a third computer process; compressing each Euler angle in a fourth computer process; offsetting each Euler angle in a fifth computer process to ensure that each angle is positive and each angle is linearly encoded; calculating a restored N×N covariance matrix from said N compressed Euler angles and N compressed eigenvectors in a sixth computer process; calculating N restored eigenvalues from said restored N×N covariance matrix in a seventh computer process; and scaling said restored N eigenvalues in an eighth computer process.