Codebook Optimization in Vector Quantization Using Genetic Algorithm
This paper presents genetic algorithm (GA) as a part of evolutionary computing for vector quantizer design in color image compression. Vector quantization, a lossy method to compress the image data in spatial domain. So the quality of the decompressed image is degraded. In order to achieve trade off...
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| creator | Chavan, P.U. Chavan, P.P. Dandawate, Y.H. |
| description | This paper presents genetic algorithm (GA) as a part of evolutionary computing for vector quantizer design in color image compression. Vector quantization, a lossy method to compress the image data in spatial domain. So the quality of the decompressed image is degraded. In order to achieve trade off between quality of compression along with good compression ratio, the vector quantizer must be designed optimally. Hence we have applied genetic algorithm on the optimal design of the codebook generation in VQ, where codebook could minimize the average distortion between a given training set and the codebook. The performance of decompression is observed by using image quality measure as PSNR for the images with RGB color space. Comparison of genetic algorithm (GA) based codebook method and random codebook method is done. |
| doi_str_mv | 10.1109/ICCEE.2009.193 |
| format | Conference Proceeding |
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Vector quantization, a lossy method to compress the image data in spatial domain. So the quality of the decompressed image is degraded. In order to achieve trade off between quality of compression along with good compression ratio, the vector quantizer must be designed optimally. Hence we have applied genetic algorithm on the optimal design of the codebook generation in VQ, where codebook could minimize the average distortion between a given training set and the codebook. The performance of decompression is observed by using image quality measure as PSNR for the images with RGB color space. 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Vector quantization, a lossy method to compress the image data in spatial domain. So the quality of the decompressed image is degraded. In order to achieve trade off between quality of compression along with good compression ratio, the vector quantizer must be designed optimally. Hence we have applied genetic algorithm on the optimal design of the codebook generation in VQ, where codebook could minimize the average distortion between a given training set and the codebook. The performance of decompression is observed by using image quality measure as PSNR for the images with RGB color space. Comparison of genetic algorithm (GA) based codebook method and random codebook method is done.</description><subject>Algorithm design and analysis</subject><subject>Color</subject><subject>Degradation</subject><subject>Distortion measurement</subject><subject>Extraterrestrial measurements</subject><subject>Genetic Algorithm</subject><subject>Genetic algorithms</subject><subject>Image coding</subject><subject>Image compression</subject><subject>Image quality</subject><subject>PSNR</subject><subject>Vector quantization</subject><isbn>9781424453658</isbn><isbn>1424453658</isbn><isbn>0769539254</isbn><isbn>9780769539256</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1jk1Lw0AURUekoK3ZunEzfyBx3nzlzbLEWAuFIli3ZZLM1NEmKcm40F_fiHo3Fy6HyyHkFlgGwMz9uijKMuOMmQyMuCBzlmujhOFKXpLE5AiSS6mEVjgj8x_OSKaNuSLJOL6zKVJxIfk1eSj6xlV9_0G3pxja8G1j6DsaOvrq6tgP9PnTdvF_3o2hO9CV61wMNV0eD_0Q4lt7Q2beHkeX_PWC7B7Ll-Ip3WxX62K5SQPkKqaVY75G65HxpqonYUBhfaUmP2BgLaLXHH2uJSAiB8eZslXuZKPAaiXFgtz9_gbn3P40hNYOX3slkEkEcQb-UU0s</recordid><startdate>200912</startdate><enddate>200912</enddate><creator>Chavan, P.U.</creator><creator>Chavan, P.P.</creator><creator>Dandawate, Y.H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200912</creationdate><title>Codebook Optimization in Vector Quantization Using Genetic Algorithm</title><author>Chavan, P.U. ; Chavan, P.P. ; Dandawate, Y.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-be0fc8af802dbc695183afb5200101aa88f628f764188821e205ab7e4d51a6543</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Algorithm design and analysis</topic><topic>Color</topic><topic>Degradation</topic><topic>Distortion measurement</topic><topic>Extraterrestrial measurements</topic><topic>Genetic Algorithm</topic><topic>Genetic algorithms</topic><topic>Image coding</topic><topic>Image compression</topic><topic>Image quality</topic><topic>PSNR</topic><topic>Vector quantization</topic><toplevel>online_resources</toplevel><creatorcontrib>Chavan, P.U.</creatorcontrib><creatorcontrib>Chavan, P.P.</creatorcontrib><creatorcontrib>Dandawate, Y.H.</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>Chavan, P.U.</au><au>Chavan, P.P.</au><au>Dandawate, Y.H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Codebook Optimization in Vector Quantization Using Genetic Algorithm</atitle><btitle>2009 Second International Conference on Computer and Electrical Engineering</btitle><stitle>ICCEE</stitle><date>2009-12</date><risdate>2009</risdate><volume>1</volume><spage>280</spage><epage>283</epage><pages>280-283</pages><isbn>9781424453658</isbn><isbn>1424453658</isbn><eisbn>0769539254</eisbn><eisbn>9780769539256</eisbn><abstract>This paper presents genetic algorithm (GA) as a part of evolutionary computing for vector quantizer design in color image compression. Vector quantization, a lossy method to compress the image data in spatial domain. So the quality of the decompressed image is degraded. In order to achieve trade off between quality of compression along with good compression ratio, the vector quantizer must be designed optimally. Hence we have applied genetic algorithm on the optimal design of the codebook generation in VQ, where codebook could minimize the average distortion between a given training set and the codebook. The performance of decompression is observed by using image quality measure as PSNR for the images with RGB color space. Comparison of genetic algorithm (GA) based codebook method and random codebook method is done.</abstract><pub>IEEE</pub><doi>10.1109/ICCEE.2009.193</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Color Degradation Distortion measurement Extraterrestrial measurements Genetic Algorithm Genetic algorithms Image coding Image compression Image quality PSNR Vector quantization |
| title | Codebook Optimization in Vector Quantization Using Genetic Algorithm |
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