Model-based sparsity projection pursuit for lattice vector quantization

In this work we present an efficient coding scheme suitable for lossy image compression using a lattice vector quantizer (LVQ) based on statistically independent data projections. The independence of these components guarantees the optimality of the quantizer. However, this introduces an overload in coding since the projection matrix rendering the components independent needs to be transmitted to the decoder. This issue is tackled by modeling the data such that the projection matrix can be recovered at the decoder side based solely on the model parameters. The original data can thus be recovered based on a reduced descriptive data model and the statistically independent components. Results show that the coding of independent components with a lattice vector quantizer is highly efficient compared with scalar or simple LVQ. Furthermore, the independent data obtained by a model-based projection shows better efficiency without the penalizing coding load of the projection matrix.