The performance and complexity of tree encoding of images in the presence of channel errors is considered. We demonstrate that a variation of the(M, L)algorithm yields performance close to the rate-distortion bound in the absence of channel errors for synthetic images modeled as two-dimensional autoregressive random fields. Trade-offs in optimizing the choice of tree search parameters are described, and experimental results on real-world images are presented. Simple tree search procedures are shown to provide signal-to-noise improvements in excess of 5 dB over conventional two-dimensional DPCM at the important rate of one bit/pixel; the effect is clear and striking to the eye. Channel error effects are treated by computer simulation and demonstrate signal-to-noise ratio improvement as high as 8 dB using tree encoding. Finally, a combined source-channel coding approach is described that exploits the significant trade-offs between source quantization accuracy and vulnerability to channel errors.