Vector quantization can improve upon scalar quantization in several ways: (1) It can lower average distortion or reduce the number of reconstruction levels by exploiting statistical dependencies between values, (2) It allows more flexible shaping of quantization regions rather than restricting them to rectangles, (3) Its granular error is affected by both the shape and size of quantization intervals rather than just size. Vector quantization generally performs better than scalar quantization when there is sample dependence or independence in the input data.