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: |
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 - Springer US, 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 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00034-019-01084-3 |
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OLC2034855345 |
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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 | |
| 650 | 4 | |a Image compression | |
| 650 | 4 | |a Discrete wavelet transform (DWT) | |
| 650 | 4 | |a Quantization | |
| 650 | 4 | |a ONIBA algorithm | |
| 650 | 4 | |a Fuzzy logic | |
| 650 | 4 | |a Image quality | |
| 650 | 4 | |a BD-PSNR | |
| 650 | 4 | |a BD-rate | |
| 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)OLC2034855345 (DE-He213)s00034-019-01084-3-p DE-627 ger DE-627 rakwb eng 600 VZ 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 ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Image compression Discrete wavelet transform (DWT) Quantization ONIBA algorithm Fuzzy logic Image quality BD-PSNR BD-rate Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Springer US, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 38 2019 8 11 03 3880-3900 |
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10.1007/s00034-019-01084-3 doi (DE-627)OLC2034855345 (DE-He213)s00034-019-01084-3-p DE-627 ger DE-627 rakwb eng 600 VZ 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 ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Image compression Discrete wavelet transform (DWT) Quantization ONIBA algorithm Fuzzy logic Image quality BD-PSNR BD-rate Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Springer US, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 38 2019 8 11 03 3880-3900 |
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10.1007/s00034-019-01084-3 doi (DE-627)OLC2034855345 (DE-He213)s00034-019-01084-3-p DE-627 ger DE-627 rakwb eng 600 VZ 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 ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Image compression Discrete wavelet transform (DWT) Quantization ONIBA algorithm Fuzzy logic Image quality BD-PSNR BD-rate Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Springer US, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 38 2019 8 11 03 3880-3900 |
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10.1007/s00034-019-01084-3 doi (DE-627)OLC2034855345 (DE-He213)s00034-019-01084-3-p DE-627 ger DE-627 rakwb eng 600 VZ 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 ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Image compression Discrete wavelet transform (DWT) Quantization ONIBA algorithm Fuzzy logic Image quality BD-PSNR BD-rate Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Springer US, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 38 2019 8 11 03 3880-3900 |
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10.1007/s00034-019-01084-3 doi (DE-627)OLC2034855345 (DE-He213)s00034-019-01084-3-p DE-627 ger DE-627 rakwb eng 600 VZ 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 ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Image compression Discrete wavelet transform (DWT) Quantization ONIBA algorithm Fuzzy logic Image quality BD-PSNR BD-rate Thakur, Kavita aut Gupta, Shubhrata aut Rao, K. R. aut Enthalten in Circuits, systems and signal processing Springer US, 1982 38(2019), 8 vom: 11. März, Seite 3880-3900 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:38 year:2019 number:8 day:11 month:03 pages:3880-3900 https://doi.org/10.1007/s00034-019-01084-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2244 AR 38 2019 8 11 03 3880-3900 |
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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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Thakur, Vikrant Singh |
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Thakur, Vikrant Singh Thakur, Kavita Gupta, Shubhrata Rao, K. R. |
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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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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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Improved Optimum Nonnegative Integer Bit Allocation Algorithm Using Fuzzy Domain Variance Estimation and Refinement for the Wavelet-Based Image Compression |
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