Lossy Data Compression with Random Gates

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...

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 Data Compression Using Logarithm

Lossy compression algorithms take advantage of the inherent limitations of the human eye and discard information that cannot be seen. [1] In the present paper a technique termed as Lossy Data Compression using Logarithm (LDCL) is proposed to compress incoming binary data in the form of a resultant matrix containing the logarithmic values of different chosen numeric sets. The proposed method is ...

Lossless and Lossy Data Compression

Data compression (or source coding) is the process of creating binary representations of data which require less storage space than the original data 7; 14; 15]. Lossless compression is used where perfect reproduction is required while lossy compression is used where perfect reproduction is not possible or requires too many bits. Achieving optimal compression with respect to resource constraint...