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,

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

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

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.