Low bit rate image coding based on vector transformation with neural network approach
Vector transformation is a new method in unifying vector quantization (VQ) and transform coding. So far, the codebook generation that has been applied in this coding is the LBG algorithm. With the development of neural networks, especially Self Organizing Feature Maps (SOFM), there are some advantag...
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| creator | Suksmono, A.B. Karsa, K. Tjondronegoro, S. Soegijoko, S. |
| description | Vector transformation is a new method in unifying vector quantization (VQ) and transform coding. So far, the codebook generation that has been applied in this coding is the LBG algorithm. With the development of neural networks, especially Self Organizing Feature Maps (SOFM), there are some advantages that can be used to improve a system's performance. In this paper, we explore the application of the SOFM algorithm to generate the Vector Transform Coding (VTC) codebook and compare the result with some coding rates using the LBG algorithm. |
| doi_str_mv | 10.1109/APCCAS.1998.743892 |
| format | Conference Proceeding |
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Proceedings (Cat. No.98EX242)</title><addtitle>APCCAS</addtitle><description>Vector transformation is a new method in unifying vector quantization (VQ) and transform coding. So far, the codebook generation that has been applied in this coding is the LBG algorithm. With the development of neural networks, especially Self Organizing Feature Maps (SOFM), there are some advantages that can be used to improve a system's performance. In this paper, we explore the application of the SOFM algorithm to generate the Vector Transform Coding (VTC) codebook and compare the result with some coding rates using the LBG algorithm.</description><subject>Bit rate</subject><subject>Discrete wavelet transforms</subject><subject>Image coding</subject><subject>Neural networks</subject><subject>Organizing</subject><subject>Signal processing</subject><subject>Signal processing algorithms</subject><subject>Transform coding</subject><subject>Vector quantization</subject><subject>Video coding</subject><isbn>0780351460</isbn><isbn>9780780351462</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1998</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8tqwzAURAWl0DbND2SlH7Crh21JS2P6AkMLTdfhyr5K1CaWkdWa_n0NyWwOzOIwQ8iGs5xzZh7q96apP3JujM5VIbURV-SOKc1kyYuK3ZD1NH2xJdJIJfUt-WzDTK1PNEJC6k-wR9qF3g97amHCnoaB_mKXQqQpwjC5EE-Q_NLOPh3ogD8RjgvSHOI3hXGMAbrDPbl2cJxwfeGKbJ8et81L1r49vzZ1m3mtUobcmb5ywkjTCUCrtascExVwaStVqVIY7ZQAzkHbjnVlbzlD1TuBBWot5IpszlqPiLsxLvPj3-78W_4DL99P_w</recordid><startdate>1998</startdate><enddate>1998</enddate><creator>Suksmono, A.B.</creator><creator>Karsa, K.</creator><creator>Tjondronegoro, S.</creator><creator>Soegijoko, S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1998</creationdate><title>Low bit rate image coding based on vector transformation with neural network approach</title><author>Suksmono, A.B. ; Karsa, K. ; Tjondronegoro, S. ; Soegijoko, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i87t-e1f9d6f2939c2aeb88f6f026a13b67675298f72a11a8bc0c5db10e7df2e4e8823</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Bit rate</topic><topic>Discrete wavelet transforms</topic><topic>Image coding</topic><topic>Neural networks</topic><topic>Organizing</topic><topic>Signal processing</topic><topic>Signal processing algorithms</topic><topic>Transform coding</topic><topic>Vector quantization</topic><topic>Video coding</topic><toplevel>online_resources</toplevel><creatorcontrib>Suksmono, A.B.</creatorcontrib><creatorcontrib>Karsa, K.</creatorcontrib><creatorcontrib>Tjondronegoro, S.</creatorcontrib><creatorcontrib>Soegijoko, S.</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 Electronic Library (IEL)</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>Suksmono, A.B.</au><au>Karsa, K.</au><au>Tjondronegoro, S.</au><au>Soegijoko, S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Low bit rate image coding based on vector transformation with neural network approach</atitle><btitle>IEEE. APCCAS 1998. 1998 IEEE Asia-Pacific Conference on Circuits and Systems. Microelectronics and Integrating Systems. Proceedings (Cat. No.98EX242)</btitle><stitle>APCCAS</stitle><date>1998</date><risdate>1998</risdate><spage>603</spage><epage>606</epage><pages>603-606</pages><isbn>0780351460</isbn><isbn>9780780351462</isbn><abstract>Vector transformation is a new method in unifying vector quantization (VQ) and transform coding. So far, the codebook generation that has been applied in this coding is the LBG algorithm. With the development of neural networks, especially Self Organizing Feature Maps (SOFM), there are some advantages that can be used to improve a system's performance. In this paper, we explore the application of the SOFM algorithm to generate the Vector Transform Coding (VTC) codebook and compare the result with some coding rates using the LBG algorithm.</abstract><pub>IEEE</pub><doi>10.1109/APCCAS.1998.743892</doi><tpages>4</tpages></addata></record> |
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| subjects | Bit rate Discrete wavelet transforms Image coding Neural networks Organizing Signal processing Signal processing algorithms Transform coding Vector quantization Video coding |
| title | Low bit rate image coding based on vector transformation with neural network approach |
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