Partitioned vector quantization: application to lossless compression of hyperspectral images

Abstract

A novel design for a vector quantizer that uses multiple codebooks of variable dimensionality is proposed. High dimensional source vectors are first partitioned into two or more subvectors of (possibly) different length and then, each subvector is individually encoded with an appropriate codebook. Further redundancy is exploited by conditional entropy coding of the subvectors indices. This scheme allows practical quantization of high dimensional vectors in which each vector component is allowed to have different alphabet and distribution. This is typically the case of the pixels representing a hyperspectral image. We present experimental results in the lossless and near-lossless encoding of such images. The method can be easily adapted to lossy coding.