Lossy compression of general random variables

Sparse Random Approximation and Lossy Compression

We discuss a method for sparse signal approximation, which is based on the correlation of the target signal with a pseudo-random signal, and uses a modification of the greedy matching pursuit algorithm. We show that this approach provides an efficient encoding-decoding method, which can be used also for lossy compression and encryption purposes.

Lossy Compression of Pattern Databases Using Acyclic Random Hypergraphs

A domain-independent heuristic function created by an abstraction is usually implemented using a Pattern Database (PDB), which is a lookup table of (abstract state, heuristic value) pairs. PDBs containing high quality heuristic values generally require substantial memory space and therefore need to be compressed. In this paper, we introduce Acyclic Random Hypergraph Compression (ARHC), a domain...

Pattern Matching and Lossy Data Compression on Random Fields

Pattern matching and lossy data compression on random fields

We consider the problem of lossy data compression for data arranged on twodimensional arrays (such as images), or more generally on higher-dimensional arrays (such as video sequences). Several of the most commonly used algorithms are based on pattern matching: Given a distortion level D and a block of data to be compressed, the encoder first finds a Dclose match of this block into some database...

Lossy Image Compression

Representing image files as simple arrays of pixels is generally very inefficient in terms of memory. Storage space and network bandwidth is limited so it is necessary to compress image files. There are many methods that achieve very high compression ratios by allowing the data to be corrupted in ways that are not easily perceivable to the human eye. A new concept is described, combining fracta...