Publication

Lossless Source Coding with Polar Codes

2010
Conference Papers

Abstract

In this paper lossless compression with polar codes is considered. A polar encoding algorithm is developed and a method to design the code and compute the average compression rate for finite lengths is given. It is shown that the scheme achieves the optimal compression rate asymptotically. Furthermore, the proposed scheme has a very good performance at finite lengths. Both the encoding and decoding operations can be accomplished with complexity O(N logN) where N denotes the length of the code.

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2010
Conference Papers

Abstract

Official source

Related concepts (32)

Data compression

In information theory, data compression, source coding, or bit-rate reduction is the process of encoding information using fewer bits than the original representation. Any particular compression is either lossy or lossless. Lossless compression reduces bits by identifying and eliminating statistical redundancy. No information is lost in lossless compression. Lossy compression reduces bits by removing unnecessary or less important information.

Lossless compression

Lossless compression is a class of data compression that allows the original data to be perfectly reconstructed from the compressed data with no loss of information. Lossless compression is possible because most real-world data exhibits statistical redundancy. By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates (and therefore reduced media sizes).

Shannon's source coding theorem

In information theory, Shannon's source coding theorem (or noiseless coding theorem) establishes the limits to possible data compression, and the operational meaning of the Shannon entropy. Named after Claude Shannon, the source coding theorem shows that (in the limit, as the length of a stream of independent and identically-distributed random variable (i.i.d.) data tends to infinity) it is impossible to compress the data such that the code rate (average number of bits per symbol) is less than the Shannon entropy of the source, without it being virtually certain that information will be lost.

Related publications (1)

Lossless Source Coding with Polar Codes

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