Multispectral Image Compression Using Universal Vector Quantization

We propose a new method for low-complexity compression of multispectral images based on universal vector quantization. Our approach generalizes the recently developed theory of universal scalar quantization to vector quantization, and uses it in the context of distributed coding. We exploit the availability of side information on the decoder to reduce the encoding rate of a vector quantizer, applied to compressed measurements of the image. The encoding reuses quantization labels to label multiple quantization cells and leverages the side information to select the correct cell at the decoder. The image is reconstructed using weighted total variation minimization, incorporating side information in the weights while enforcing consistency with the recovered quantization cell.