Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression
Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on...
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| Autor*in: |
Thakur, Vikrant Singh [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Circuits, systems and signal processing - Boston, Mass. : Birkhäuser, 1982, 38(2019), 8 vom: 11. März, Seite 3880-3900 |
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| Übergeordnetes Werk: |
volume:38 ; year:2019 ; number:8 ; day:11 ; month:03 ; pages:3880-3900 |
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| DOI / URN: |
10.1007/s00034-019-01084-3 |
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| 520 | |a Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms. | ||
| 650 | 4 | |a Transform coding |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image compression |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Discrete wavelet transform (DWT) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Quantization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a ONIBA algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fuzzy logic |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image quality |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a BD-PSNR |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a BD-rate |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Thakur, Kavita |4 aut | |
| 700 | 1 | |a Gupta, Shubhrata |4 aut | |
| 700 | 1 | |a Rao, K. R. |4 aut | |
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10.1007/s00034-019-01084-3 doi (DE-627)SPR000599980 (SPR)s00034-019-01084-3-e DE-627 ger DE-627 rakwb eng Thakur, Vikrant Singh verfasserin (orcid)0000-0003-3836-9586 aut Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms. Transform coding (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Discrete wavelet transform (DWT) (dpeaa)DE-He213 Quantization (dpeaa)DE-He213 ONIBA algorithm (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 BD-PSNR (dpeaa)DE-He213 BD-rate (dpeaa)DE-He213 Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://dx.doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2019 8 11 03 3880-3900 |
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10.1007/s00034-019-01084-3 doi (DE-627)SPR000599980 (SPR)s00034-019-01084-3-e DE-627 ger DE-627 rakwb eng Thakur, Vikrant Singh verfasserin (orcid)0000-0003-3836-9586 aut Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms. Transform coding (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Discrete wavelet transform (DWT) (dpeaa)DE-He213 Quantization (dpeaa)DE-He213 ONIBA algorithm (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 BD-PSNR (dpeaa)DE-He213 BD-rate (dpeaa)DE-He213 Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://dx.doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2019 8 11 03 3880-3900 |
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10.1007/s00034-019-01084-3 doi (DE-627)SPR000599980 (SPR)s00034-019-01084-3-e DE-627 ger DE-627 rakwb eng Thakur, Vikrant Singh verfasserin (orcid)0000-0003-3836-9586 aut Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms. Transform coding (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Discrete wavelet transform (DWT) (dpeaa)DE-He213 Quantization (dpeaa)DE-He213 ONIBA algorithm (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 BD-PSNR (dpeaa)DE-He213 BD-rate (dpeaa)DE-He213 Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://dx.doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2019 8 11 03 3880-3900 |
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10.1007/s00034-019-01084-3 doi (DE-627)SPR000599980 (SPR)s00034-019-01084-3-e DE-627 ger DE-627 rakwb eng Thakur, Vikrant Singh verfasserin (orcid)0000-0003-3836-9586 aut Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms. Transform coding (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Discrete wavelet transform (DWT) (dpeaa)DE-He213 Quantization (dpeaa)DE-He213 ONIBA algorithm (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 BD-PSNR (dpeaa)DE-He213 BD-rate (dpeaa)DE-He213 Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://dx.doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2019 8 11 03 3880-3900 |
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10.1007/s00034-019-01084-3 doi (DE-627)SPR000599980 (SPR)s00034-019-01084-3-e DE-627 ger DE-627 rakwb eng Thakur, Vikrant Singh verfasserin (orcid)0000-0003-3836-9586 aut Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms. Transform coding (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Discrete wavelet transform (DWT) (dpeaa)DE-He213 Quantization (dpeaa)DE-He213 ONIBA algorithm (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 BD-PSNR (dpeaa)DE-He213 BD-rate (dpeaa)DE-He213 Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://dx.doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2019 8 11 03 3880-3900 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR000599980</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230330083848.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201001s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00034-019-01084-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR000599980</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00034-019-01084-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Thakur, Vikrant Singh</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3836-9586</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. 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Thakur, Vikrant Singh |
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Thakur, Vikrant Singh misc Transform coding misc Image compression misc Discrete wavelet transform (DWT) misc Quantization misc ONIBA algorithm misc Fuzzy logic misc Image quality misc BD-PSNR misc BD-rate Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression |
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Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression Transform coding (dpeaa)DE-He213 Image compression (dpeaa)DE-He213 Discrete wavelet transform (DWT) (dpeaa)DE-He213 Quantization (dpeaa)DE-He213 ONIBA algorithm (dpeaa)DE-He213 Fuzzy logic (dpeaa)DE-He213 Image quality (dpeaa)DE-He213 BD-PSNR (dpeaa)DE-He213 BD-rate (dpeaa)DE-He213 |
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misc Transform coding misc Image compression misc Discrete wavelet transform (DWT) misc Quantization misc ONIBA algorithm misc Fuzzy logic misc Image quality misc BD-PSNR misc BD-rate |
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Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression |
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Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression |
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improved optimum nonnegative integer bit allocation algorithm using fuzzy domain variance estimation and refinement for the wavelet-based image compression |
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Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression |
| abstract |
Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstractGer |
Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract Optimum nonnegative integer bit allocation (ONIBA) is a conspicuous technique, which usually provides the solution of optimal quantization issues for the transform coders (TCs). In order to obtain the optimum bits for a specific quantizer, all the existing ONIBA algorithms strongly rely on the variance characteristics of transform coefficients. Typically, in the wavelet-based TCs, the sub-band variances are directly estimated in the wavelet domain. This direct variance estimation is not supposed to be the best way to obtain the exact variance information, because the practical values of the wavelet coefficients may not be precise and therefore constitute an uncertain environment for the accurate variance estimation. Consequently, all the existing ONIBA algorithms often exhibit poor quantization performance in the presence of entropy coder. Hence, this paper presents a new fuzzy domain variance estimation and refinement (FDVER)-based ONIBA algorithm to attain the real optimum quantization of the wavelet coefficients in the presence of entropy coder. The outcome shows that the proposed FDVER-ONIBA algorithm outperforms and provides high-quality image compression along with the significant bitrate savings by the efficient quantization of the wavelet coefficients as compared to the existing common sub-band coding technique and the recent ONIBA algorithms. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| title_short |
Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression |
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https://dx.doi.org/10.1007/s00034-019-01084-3 |
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Thakur, Kavita Gupta, Shubhrata Rao, K. R. |
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2024-07-03T17:12:28.144Z |
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| score |
7.3985195 |