In this work, a comparative study between Elliptic Fourier and B-spline descriptors is carried out for comparing their efficiency in characterizing the contour shape of image objects. In both cases, the goal is to obtain the least representation error using the fewest possible number of coefficients. With Fourier descriptors, different number of harmonics are used while the remaining ones are set to zero. In the B-spline case, coefficients are obtained iteratively using a least-square filter, followed by a decimation procedure. Linear and cubic B-splines are considered. In general, data will be more compressed when the lower number of coefficients is used, but the representation error also increases considerably. We use a signal/error ratio, expressed in dBs, to measure the similarity of each approximation. The signal value is obtained from the 'modulo' addition of all coordinate points, whereas the error value is computed accumulating the 'modulo' distance between original and reconstructed shape. It can be shown that for a lower compression rate, the results do not vary significantly in all three methods. For higher compression rates, Elliptic Fourier Descriptors are more efficient than linear and cubic B-splines, especially in soft contours, but B-splines have lower computational cost.