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Distributed Quantization of Correlated Images

This work describes a practical implementation of a theoretically optimal distributed source quantization algorithm applied to the problem of encoding correlated images with separate encoders and a common decoder. We develop multi-dimensional codebooks based on a distributed form of the generalized Lloyd algorithm. We model image patches as elements of a Gaussian mixture model and show how a two-dimensional codebook of image patch codevectors can be used to significantly compress one image by using the reconstructed second image as partial side information. To test the approach we experiment with multimodal images of highly correlated electro-optical and infrared images and show the performance advantage of distributed source quantization over conventional non-distributed lossy compression.