Large Alphabet Source Coding Using Independent Component Analysis
Large alphabet source coding is a basic and well-studied problem in data compression. It has many applications, such as compression of natural language text, speech, and images. The classic perception of most commonly used methods is that a source is best described over an alphabet, which is at leas...
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Gespeichert in:
| Autor*in: |
Painsky, Amichai [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on information theory - Piscataway, NJ : IEEE, 1963, 63(2017), 10, Seite 6514-6529 |
|---|---|
| Übergeordnetes Werk: |
volume:63 ; year:2017 ; number:10 ; pages:6514-6529 |
| Links: |
|---|
| DOI / URN: |
10.1109/TIT.2017.2728017 |
|---|
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OLC1996944088 |
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| 520 | |a Large alphabet source coding is a basic and well-studied problem in data compression. It has many applications, such as compression of natural language text, speech, and images. The classic perception of most commonly used methods is that a source is best described over an alphabet, which is at least as large as the observed alphabet. In this paper, we challenge this approach and introduce a conceptual framework in which a large alphabet source is decomposed into "as statistically independent as possible" components. This decomposition allows us to apply entropy encoding to each component separately, while benefiting from their reduced alphabet size. We show that in many cases, such decomposition results in a sum of marginal entropies which is only slightly greater than the entropy of the source. Our suggested algorithm, based on a generalization of the binary independent component analysis, is applicable for a variety of large alphabet source coding setups. This includes the classical lossless compression, universal compression, and high-dimensional vector quantization. In each of these setups, our suggested approach outperforms most commonly used methods. Moreover, our proposed framework is significantly easier to implement in most of these cases. | ||
| 650 | 4 | |a Independent component analysis | |
| 650 | 4 | |a Source coding | |
| 650 | 4 | |a Entropy Coding | |
| 650 | 4 | |a Decoding | |
| 650 | 4 | |a Redundancy | |
| 650 | 4 | |a Data Compression | |
| 650 | 4 | |a Entropy | |
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| 700 | 1 | |a Feder, Meir |4 oth | |
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10.1109/TIT.2017.2728017 doi PQ20171228 (DE-627)OLC1996944088 (DE-599)GBVOLC1996944088 (PRQ)i657-fd2c304c29599d7b084a47a4126702c653d332b174d5829971ea83d0b7f3c8f0 (KEY)0023448620170000063001006514largealphabetsourcecodingusingindependentcomponent DE-627 ger DE-627 rakwb eng 070 620 DE-600 SA 5570 AVZ rvk Painsky, Amichai verfasserin aut Large Alphabet Source Coding Using Independent Component Analysis 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Large alphabet source coding is a basic and well-studied problem in data compression. It has many applications, such as compression of natural language text, speech, and images. The classic perception of most commonly used methods is that a source is best described over an alphabet, which is at least as large as the observed alphabet. In this paper, we challenge this approach and introduce a conceptual framework in which a large alphabet source is decomposed into "as statistically independent as possible" components. This decomposition allows us to apply entropy encoding to each component separately, while benefiting from their reduced alphabet size. We show that in many cases, such decomposition results in a sum of marginal entropies which is only slightly greater than the entropy of the source. Our suggested algorithm, based on a generalization of the binary independent component analysis, is applicable for a variety of large alphabet source coding setups. This includes the classical lossless compression, universal compression, and high-dimensional vector quantization. In each of these setups, our suggested approach outperforms most commonly used methods. Moreover, our proposed framework is significantly easier to implement in most of these cases. Independent component analysis Source coding Entropy Coding Decoding Redundancy Data Compression Entropy Rosset, Saharon oth Feder, Meir oth Enthalten in IEEE transactions on information theory Piscataway, NJ : IEEE, 1963 63(2017), 10, Seite 6514-6529 (DE-627)12954731X (DE-600)218505-2 (DE-576)01499819X 0018-9448 nnns volume:63 year:2017 number:10 pages:6514-6529 http://dx.doi.org/10.1109/TIT.2017.2728017 Volltext http://ieeexplore.ieee.org/document/7983009 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI GBV_ILN_65 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2088 SA 5570 AR 63 2017 10 6514-6529 |
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Large Alphabet Source Coding Using Independent Component Analysis |
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Large Alphabet Source Coding Using Independent Component Analysis |
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Painsky, Amichai |
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large alphabet source coding using independent component analysis |
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Large Alphabet Source Coding Using Independent Component Analysis |
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Large alphabet source coding is a basic and well-studied problem in data compression. It has many applications, such as compression of natural language text, speech, and images. The classic perception of most commonly used methods is that a source is best described over an alphabet, which is at least as large as the observed alphabet. In this paper, we challenge this approach and introduce a conceptual framework in which a large alphabet source is decomposed into "as statistically independent as possible" components. This decomposition allows us to apply entropy encoding to each component separately, while benefiting from their reduced alphabet size. We show that in many cases, such decomposition results in a sum of marginal entropies which is only slightly greater than the entropy of the source. Our suggested algorithm, based on a generalization of the binary independent component analysis, is applicable for a variety of large alphabet source coding setups. This includes the classical lossless compression, universal compression, and high-dimensional vector quantization. In each of these setups, our suggested approach outperforms most commonly used methods. Moreover, our proposed framework is significantly easier to implement in most of these cases. |
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Large alphabet source coding is a basic and well-studied problem in data compression. It has many applications, such as compression of natural language text, speech, and images. The classic perception of most commonly used methods is that a source is best described over an alphabet, which is at least as large as the observed alphabet. In this paper, we challenge this approach and introduce a conceptual framework in which a large alphabet source is decomposed into "as statistically independent as possible" components. This decomposition allows us to apply entropy encoding to each component separately, while benefiting from their reduced alphabet size. We show that in many cases, such decomposition results in a sum of marginal entropies which is only slightly greater than the entropy of the source. Our suggested algorithm, based on a generalization of the binary independent component analysis, is applicable for a variety of large alphabet source coding setups. This includes the classical lossless compression, universal compression, and high-dimensional vector quantization. In each of these setups, our suggested approach outperforms most commonly used methods. Moreover, our proposed framework is significantly easier to implement in most of these cases. |
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Large alphabet source coding is a basic and well-studied problem in data compression. It has many applications, such as compression of natural language text, speech, and images. The classic perception of most commonly used methods is that a source is best described over an alphabet, which is at least as large as the observed alphabet. In this paper, we challenge this approach and introduce a conceptual framework in which a large alphabet source is decomposed into "as statistically independent as possible" components. This decomposition allows us to apply entropy encoding to each component separately, while benefiting from their reduced alphabet size. We show that in many cases, such decomposition results in a sum of marginal entropies which is only slightly greater than the entropy of the source. Our suggested algorithm, based on a generalization of the binary independent component analysis, is applicable for a variety of large alphabet source coding setups. This includes the classical lossless compression, universal compression, and high-dimensional vector quantization. In each of these setups, our suggested approach outperforms most commonly used methods. Moreover, our proposed framework is significantly easier to implement in most of these cases. |
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Large Alphabet Source Coding Using Independent Component Analysis |
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