Regularized Residual Quantization: a multi-layer sparse dictionary learning approach

Understand

The Residual Quantization (RQ) framework is revisited where the quantization distortion is being successively reduced in multi-layers.

• Inspired by the reverse-water-filling paradigm in rate-distortion theory, an efficient regularization on the variances of the codewords is introduced which allows to extend the RQ for very large numbers of layers and also for high dimensional data, without getting over-trained.
• The proposed Regularized Residual Quantization (RRQ) results in multi-layer dictionaries which are additionally sparse, thanks to the soft-thresholding nature of the regularization when applied to variance-decaying data which can arise from de-correlating transformations applied to correlated data.
• Furthermore, we also propose a general-purpose pre-processing for natural images which makes them suitable for such quantization.