In this paper, we present a memory-efficient, contour-based, region-of-interest (ROI) algorithm designed for ultra-low-bit-rate compression of very large images. The proposed technique is integrated into a user-interactive wavelet-based image coding system in which multiple ROIs of any shape and size can be selected and coded efficiently. The coding technique compresses region-of-interest and background (non-ROI) information independently by allocating more bits to the selected targets and fewer bits to the background data. This allows the user to transmit large images at very low bandwidths with lossy/lossless ROI coding, while preserving the background content to a certain level for contextual purposes. Extremely large images (e.g., 65000 × 65000 pixels) with multiple large ROIs can be coded with minimal memory usage by using intelligent ROI tiling techniques. The foreground information at the encoder/decoder is independently extracted for each tile without adding extra ROI side information to the bit stream. The arbitrary ROI contour is down-sampled and differential chain coded (DCC) for efficient transmission. ROI wavelet masks for each tile are generated and processed independently to handle any size image and any shape/size of overlapping ROIs. The resulting system dramatically reduces the data storage and transmission bandwidth requirements for large digital images with multiple ROIs.