Lossy compression of tree architectures

Using general codes and distortion measures we obtain a theory of lossy compression: Given a model family (code word set) and a particular distortion measure or “loss” measure,

Nous pouvons r´esumer deux types de m´ethodes de classification de donn´ees : les m´ethodes de partitionnement hi´erarchique soit construisent des amas d’objets

If a classifier is trained for original (uncompressed) data and then applied to compressed images, then more compression (a larger CR, a smaller PSNR or PSNR-HVS-M) leads

We use the Varint GB [5] compression schema. In order to take into account the structure of the graph, we use three labelling functions which label the nodes of the graph in

In contrast, since it snaps values to the middle of intervals, simple range quantization [27] may alter the persistence of critical point pairs in the decompressed data, by

The error committed for the proposed algorithm with a subsampling of factor 2 (1 bit per pixel) is similar to the one obtained with JPEG compression with 3 bits per pixel.. The

Using a unique transform learned via autoencoders and var- ious quantization step sizes at test time, it is possible to compress as well as when learning one transform per

In this section, we assess the rate-distortion perfor- mance of the proposed scheme. The rate is measured in bits per original source symbol, as output by the zero-error SW encoder.