Lossy Compression

The reduction or ideally elimination of redundancies in the original data to result in smaller required storage space is the goal of every compression scheme. There are two categories of data compression: lossy and lossless. Lossy compression is achieved by only discarding the redundancies and out of human perception information and getting rid of those extra bits.

Parameters

$n$ : number of items in input series of data

Filters

Computational Model

Randomization

Approximation

Algorithms Table

Displaying 12 of 12 algorithms


Sun; M. Shao; J. Chen; K. Wong; and X. Wu	2010	$O(kmn)$	$O(kmn)$
Jalali; A. Montanari; and T. Weissman	2010	$O(n)$	$O(n)$
Korada and R. Urbanke;	2010	$O(n 2^n)$	$O(N)$
Gupta; Verdu	2009	$O(n^3 \log n)$	$O(n)$
Jalali and T. Weissman	2008	$O(n)$	$O(n)$
Miyake 2006	2006	$O(n 2^n)$	$O(2^n)$
Martinian and M. J. Wainwright	2006	$O(n 2^n)$	$O(mn+mk)$
Maneva and M. J. Wainwright	2005	$O(n^2)$	$O(n^2)$
Ciliberti; Mézard	2005	$O(n^2)$	$O(n^2)$
Matsunaga; Yamamoto	2003	$O(n 2^n)$	$\exp(n)$
Discrete Cosine Transform	1974	$O(n \log n)$	$O(n)$
Brute force	1940	$O(n 2^n)$	$O(n2^n)$

Reductions Table

Insuffient Data to display table

Other relevant algorithms