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Retrieving quantized signal from its noisy version
In this paper we propose an algorithm to retrieve a quantized data from its noisy version. To find the optimum quantization levels, a multistage process minimizes the Mean Square Error (MSE) at each quantization level by using the Minimum Noiseless Description Length (MNDL) algorithm. Consequently,...
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| creator | Hashemi, SayedMasoud Beheshti, Soosan |
| description | In this paper we propose an algorithm to retrieve a quantized data from its noisy version. To find the optimum quantization levels, a multistage process minimizes the Mean Square Error (MSE) at each quantization level by using the Minimum Noiseless Description Length (MNDL) algorithm. Consequently, the procedure denoises and recovers the quantized data simultaneously. The prior knowledge that the original signal is a quantized data enables us to denoise the data more efficiently. We show that in high Signal to Noise Ratio (SNR) cases, the retrieved levels are the same as the original levels of the quantized signal. However, in low SNR cases, since the quantized signal has been highly effected by the additive noise, the optimum retrieved levels are less than the original quantization levels. |
| doi_str_mv | 10.1109/SIPS.2010.5624890 |
| format | conference_proceeding |
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To find the optimum quantization levels, a multistage process minimizes the Mean Square Error (MSE) at each quantization level by using the Minimum Noiseless Description Length (MNDL) algorithm. Consequently, the procedure denoises and recovers the quantized data simultaneously. The prior knowledge that the original signal is a quantized data enables us to denoise the data more efficiently. We show that in high Signal to Noise Ratio (SNR) cases, the retrieved levels are the same as the original levels of the quantized signal. 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To find the optimum quantization levels, a multistage process minimizes the Mean Square Error (MSE) at each quantization level by using the Minimum Noiseless Description Length (MNDL) algorithm. Consequently, the procedure denoises and recovers the quantized data simultaneously. The prior knowledge that the original signal is a quantized data enables us to denoise the data more efficiently. We show that in high Signal to Noise Ratio (SNR) cases, the retrieved levels are the same as the original levels of the quantized signal. However, in low SNR cases, since the quantized signal has been highly effected by the additive noise, the optimum retrieved levels are less than the original quantization levels.</description><subject>Additive noise</subject><subject>Gaussian noise</subject><subject>Noise measurement</subject><subject>Noise reduction</subject><subject>Quantization</subject><subject>Signal to noise ratio</subject><subject>Wavelet transforms</subject><issn>2162-3562</issn><issn>2162-3570</issn><isbn>1424489326</isbn><isbn>9781424489329</isbn><isbn>9781424489343</isbn><isbn>1424489334</isbn><isbn>9781424489336</isbn><isbn>1424489342</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9j91qAjEQRtM_qFofoPQmL7A2M5lNNpdFaisIFu29rLsTSdFsu9kK9ukr1Hr1cThw4BPiHtQIQLnH5fRtOUJ1xNwgFU5diKGzBRDSkTTpS9FDMJjp3Kor0f8XaK7PwuCt6Kf0oZShHE1P4IK7NvA-xI38-i5jF364lilsYrmVvm12MnRJxiakg9xzm0IT78SNL7eJh6cdiMXk-X38ms3mL9Px0ywLTnWZN_UaEGoHTLoC1AUpsr5mLLR1DOQqD5YNG0PsCuWdzwm48qzXpdYD8fAXDcy8-mzDrmwPq9Nx_QsKNkgc</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Hashemi, SayedMasoud</creator><creator>Beheshti, Soosan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201010</creationdate><title>Retrieving quantized signal from its noisy version</title><author>Hashemi, SayedMasoud ; Beheshti, Soosan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-f6db121d91e43c12384047fde28379e149cf17e6e664e980f9f541ecfe3ba33</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Additive noise</topic><topic>Gaussian noise</topic><topic>Noise measurement</topic><topic>Noise reduction</topic><topic>Quantization</topic><topic>Signal to noise ratio</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Hashemi, SayedMasoud</creatorcontrib><creatorcontrib>Beheshti, Soosan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hashemi, SayedMasoud</au><au>Beheshti, Soosan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Retrieving quantized signal from its noisy version</atitle><btitle>2010 IEEE Workshop On Signal Processing Systems</btitle><stitle>SIPS</stitle><date>2010-10</date><risdate>2010</risdate><spage>456</spage><epage>461</epage><pages>456-461</pages><issn>2162-3562</issn><eissn>2162-3570</eissn><isbn>1424489326</isbn><isbn>9781424489329</isbn><eisbn>9781424489343</eisbn><eisbn>1424489334</eisbn><eisbn>9781424489336</eisbn><eisbn>1424489342</eisbn><abstract>In this paper we propose an algorithm to retrieve a quantized data from its noisy version. To find the optimum quantization levels, a multistage process minimizes the Mean Square Error (MSE) at each quantization level by using the Minimum Noiseless Description Length (MNDL) algorithm. Consequently, the procedure denoises and recovers the quantized data simultaneously. The prior knowledge that the original signal is a quantized data enables us to denoise the data more efficiently. We show that in high Signal to Noise Ratio (SNR) cases, the retrieved levels are the same as the original levels of the quantized signal. However, in low SNR cases, since the quantized signal has been highly effected by the additive noise, the optimum retrieved levels are less than the original quantization levels.</abstract><pub>IEEE</pub><doi>10.1109/SIPS.2010.5624890</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 2162-3562 |
| ispartof | 2010 IEEE Workshop On Signal Processing Systems, 2010, p.456-461 |
| issn | 2162-3562 2162-3570 |
| language | eng |
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| source | IEEE Xplore All Conference Series |
| subjects | Additive noise Gaussian noise Noise measurement Noise reduction Quantization Signal to noise ratio Wavelet transforms |
| title | Retrieving quantized signal from its noisy version |
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