In this paper, we introduce a novel transform-based image compression framework called local zerotree (LZT) coding. The main idea is to partition the transform coefficients into small groups, each of which is encoded independently using popular zerotree algorithms such as EZW or SPIHT. The advantage of the new coding algorithm is fourfold: (i) because of the reduction of memory buffering, LZT can reduce the complexity of the codec implementation and increase the speed of the zerotree algorithm significantly, especially in hardware; (ii) LZT is capable of processing large images under limited memory constraint; (iii) LZT supports parallel processing mode as long as the transform in use has that capability; and (iv) LZT facilitates the coding/decoding of regions of interest. Moreover, we shall demonstrate that the penalty in coding performance is minute comparing to its global zerotree predecessors: there are only a few extra bytes of side information. Essentially, the only property that LZT sacrifices is the embeddedness, which might not be crucial in many applications.