The set partitioning in hierarchical trees (SPIHT) approach for still-image compression proposed by Said and Pearlman (1996) is one of the most efficient embedded monochrome image compression schemes known to date. The algorithm relies on a very efficient scanning and bit-allocation scheme for quantizing the coefficients obtained by a wavelet decomposition of an image. In this paper, we adopt this approach to scan groups (vectors) of wavelet coefficients, and use successive refinement vector quantization (VQ) techniques with staggered bit-allocation to quantize the groups at once. The scheme is named vector SPIHT (VSPIHT). We present discussions on possible models for the distributions of the coefficient vectors, and show how trained classified tree-multistage VQ techniques can be used to efficiently quantize them. Extensive coding results comparing VSPIHT to scalar SPIHT in the mean-squared-error sense, are presented for monochrome images. VSPIHT is found to yield superior performance for most images, especially those with high detail content. The method is also applied to color video coding, where a partially scalable bitstream is generated. We present the coding results on QCIF sequences as compared against H.263.