A quasi-periodic signal is a periodic signal with period and amplitude variations. The electrocardiogram (ECG) and several physiological signals can be treated as quasi-periodic. Vector quantization (VQ) is a valuable and universal tool for signal compression. However, the periodicity of a quasi-periodic signal causes data redundancy in the VQ codebook, where many codevectors are highly correlated. This paper explores the codebook (CB) redundancy in order to increase storage efficiency for physiological quasi-periodic signals. A quantitative CB redundancy measure and two redundancy reducing algorithms are proposed. Both algorithms use a mixed CB structure containing one and two-dimensional CBs. The first algorithm is applied to a CB directly, and the second one uses an LBG-like training algorithm to obtain a storage-efficient CB from a set of training vectors. With the MIT/BIH ECG database, the experimental results show that both algorithms can reduce the CB redundancy effectively with essentially no loss of signal quality. For comparison, the mean-shape VQ (MSVQ) proposed by Cárdenas-Barrera and Lorenzo-Ginori for ECG compression is implemented and the resulting average percent of the root-mean-square difference (PRD) is 10.78%. By using the first algorithm, the CB storage space is reduced by 40% and the resulting average PRD is 10.87%. The second algorithm can reduce the CB storage space by 75% and the average PRD is 10.27%, which is even better than the original MSVQ.