The Experts below are selected from a list of 474 Experts worldwide ranked by ideXlab platform
Michel Barlaud - One of the best experts on this subject based on the ideXlab platform.
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OPTIMAL WEIGHTED MODEL-BASED Bit Allocation FOR QUINCUNX SAMPLED IMAGES
2020Co-Authors: Annabelle Gouze, Marc Antonini, Christophe Parisot, Michel BarlaudAbstract:ABSTRACT In this paper we address the problem of quincunx sampled images compression. Our objective is to define an efficient Bit Allocation method adapted to quincunx sampled images. We first estimate the subband optimal weightings for a global distortion estimation. These weightings are derived from the filters used to perform the quincunx wavelet transform. Then, we use our weightings in a model-based Bit Allocation Procedure. Our method uses generalized Gaussians to approximate the probability density function of the wavelet coefficients in each subband. The Bit Allocation method which is proposed provides both low complexity and high performance
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Research Article JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding
2015Co-Authors: Marco Cagnazzo, Marc Antonini, Michel Barlaud, Recommended James, E. FowlerAbstract:We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted Bit Allocation Procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured. Copyright © 2007 Thomas Andre ́ et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1
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JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding
EURASIP Journal on Image and Video Processing, 2007Co-Authors: Thomas André, Marc Antonini, Marco Cagnazzo, Michel BarlaudAbstract:We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted Bit Allocation Procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured.
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Multiple Description Video Coding for UMTS
2003Co-Authors: Manuela Pereira, Marc Antonini, Michel BarlaudAbstract:We consider the problem of efficient real time video transmission over UMTS channels. Such a problem involves good compression rates and effectiveness in presence of channel failures. We propose a balanced MDC scheme for 3D scan-based DWT video coding. This MDC includes an efficient Bit Allocation Procedure that dispatches the source video redundancy between the different channels[1, 2]. We propose to adapt the redundancy between the descriptions according to the channel model and time-varying state (BER). We evaluate the performance of the proposed MDC video coder for two descriptors and present the results obtained for transmission over an UMTS channel. We compare the proposed method to Singular Description Coding (SDC) with a similar codec. 1
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Channel adapted multiple description coding scheme using wavelet transform
2002Co-Authors: Manuela Pereira, Marc Antonini, Michel BarlaudAbstract:A challenge of image communication over unreliable chan-nels is to achieve good compression rates and be effective in presence of channel failures. In this work we use the Multiple Description Coding (MDC) techniques, based on Wavelet Transforms, that have been shown to be powerful against channel failures. We propose a Bit Allocation Procedure that dispatch redundancy between the different channels when compressing to a tar-get Bit rate with a bounded side distortion. In this way we develop a MDC scheme well adapted to channel noise
Marc Antonini - One of the best experts on this subject based on the ideXlab platform.
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OPTIMAL WEIGHTED MODEL-BASED Bit Allocation FOR QUINCUNX SAMPLED IMAGES
2020Co-Authors: Annabelle Gouze, Marc Antonini, Christophe Parisot, Michel BarlaudAbstract:ABSTRACT In this paper we address the problem of quincunx sampled images compression. Our objective is to define an efficient Bit Allocation method adapted to quincunx sampled images. We first estimate the subband optimal weightings for a global distortion estimation. These weightings are derived from the filters used to perform the quincunx wavelet transform. Then, we use our weightings in a model-based Bit Allocation Procedure. Our method uses generalized Gaussians to approximate the probability density function of the wavelet coefficients in each subband. The Bit Allocation method which is proposed provides both low complexity and high performance
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Research Article JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding
2015Co-Authors: Marco Cagnazzo, Marc Antonini, Michel Barlaud, Recommended James, E. FowlerAbstract:We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted Bit Allocation Procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured. Copyright © 2007 Thomas Andre ́ et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1
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JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding
EURASIP Journal on Image and Video Processing, 2007Co-Authors: Thomas André, Marc Antonini, Marco Cagnazzo, Michel BarlaudAbstract:We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted Bit Allocation Procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured.
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Multiple Description Video Coding for UMTS
2003Co-Authors: Manuela Pereira, Marc Antonini, Michel BarlaudAbstract:We consider the problem of efficient real time video transmission over UMTS channels. Such a problem involves good compression rates and effectiveness in presence of channel failures. We propose a balanced MDC scheme for 3D scan-based DWT video coding. This MDC includes an efficient Bit Allocation Procedure that dispatches the source video redundancy between the different channels[1, 2]. We propose to adapt the redundancy between the descriptions according to the channel model and time-varying state (BER). We evaluate the performance of the proposed MDC video coder for two descriptors and present the results obtained for transmission over an UMTS channel. We compare the proposed method to Singular Description Coding (SDC) with a similar codec. 1
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Channel adapted multiple description coding scheme using wavelet transform
2002Co-Authors: Manuela Pereira, Marc Antonini, Michel BarlaudAbstract:A challenge of image communication over unreliable chan-nels is to achieve good compression rates and be effective in presence of channel failures. In this work we use the Multiple Description Coding (MDC) techniques, based on Wavelet Transforms, that have been shown to be powerful against channel failures. We propose a Bit Allocation Procedure that dispatch redundancy between the different channels when compressing to a tar-get Bit rate with a bounded side distortion. In this way we develop a MDC scheme well adapted to channel noise
Xinhua Zhuang - One of the best experts on this subject based on the ideXlab platform.
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A NOVEL DATA REPRESENTATION STRATEGY FOR WAVELET IMAGE COMPRESSION
2008Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. Several very competitive wavelet coders have been developed, namely, Shapiro's embedded zerotree wavelets (EZW), Servetto et al.'s morphological representation of wavelet data (MRWD), and Said and Pearlman's set partitioning in hierarchical trees (SPIHT). In this paper, we develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that exploits both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments show that the proposed SLCCA outperforms all three aforementioned wavelet coders. For example, for the "Barbara" image, at 0.50 bpp SLCCA outperforms EZW and SPIHT by 1.75 dB and 0.89 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with large texture regions. For eight typical 256 256 grayscale texture images compressed at 0.40 bpp, SLCCA outperforms SPIHT by 0.32 dB{ 0.70 dB. This outstanding performance is achieved without any optimal Bit Allocation Procedure. Thus both the encoding and decoding Procedures are fast
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significance linked connected component analysis for wavelet image coding
IEEE Transactions on Image Processing, 1999Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:The success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's (1993) embedded zerotree wavelets (EZW), Servetto et al.'s (1995) morphological representation of wavelet data (MRWD), and Said and Pearlman's (see IEEE Trans. Circuits Syst. Video Technol., vol.6, p.245-50, 1996) set partitioning in hierarchical trees (SPIHT). We develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, MRWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256/spl times/256 grayscale texture images compressed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This performance is achieved without using any optimal Bit Allocation Procedure. Thus both the encoding and decoding Procedures are fast.
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Significance-linked connected component analysis for wavelet image coding
1999Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:Abstract- Recent success in wavelet image coding is mainly attributed to the recognition of importance of data organization and representation. There have beenseveral very competitive wavelet coders developed, namely, embedded zerotree wavelets (EZW), morphological representation of wavelet data (MRWD), and set partitioning in hierarchical trees (SPIHT). In this paper, we developanovel wavelet image coder called signi cance-linked connected component analysis (SLCCA) of wavelet coe cients that extends MRWD by exploiting both within-subband clustering of signi cant coe cients and cross-subband dependency in significant elds. Computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT as well. For example, for the \Barbara " image, at 0.50 bpp SLCCA outperforms EZW and SPIHT by 1.71 dB and 0.85 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. This outstanding performance is achieved without using any optimal Bit Allocation Procedure. Thus both the encoding and decoding Procedures are fast
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Significance-linked connected component analysis for wavelet image coding
1997Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's embedded zerotree wavelets (EZW), Servetto et al.'s morphological representation of wavelet data (MRWD), and Said and Pearlman's set partitioning in hierarchical trees (SPIHT). In this paper, we develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the \Barbara " image, at 0.5 bpp SLCCA outperforms EZW and SPIHT by 1.75 dB and 0.89 dB in PSNR, respectively. This outstanding performance is achieved without using any optimal Bit Allocation Procedure, thus both the encoding and decoding Procedures are fast
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Significance-linked connected component analysis for wavelet image coding
1997Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's embedded zerotree wavelets (EZW), Servetto et al.'s morphological representation of wavelet data (MRWD), and Said and Pearlman's set partitioning in hierarchical trees (SPIHT). In this paper, we developanovel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the "Barbara" image, at 0.25 bpp SLCCA outperforms EZW, MRWD and SPIHT by 1.41 dB, 0.32 dB and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256 256 grayscale texture images compressed at 0.40 bpp, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This outstanding performance is achieved without using any optimal Bit Allocation Procedure. Thus both the encoding and decoding Procedures are fast
Bingbing Chai - One of the best experts on this subject based on the ideXlab platform.
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A NOVEL DATA REPRESENTATION STRATEGY FOR WAVELET IMAGE COMPRESSION
2008Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. Several very competitive wavelet coders have been developed, namely, Shapiro's embedded zerotree wavelets (EZW), Servetto et al.'s morphological representation of wavelet data (MRWD), and Said and Pearlman's set partitioning in hierarchical trees (SPIHT). In this paper, we develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that exploits both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments show that the proposed SLCCA outperforms all three aforementioned wavelet coders. For example, for the "Barbara" image, at 0.50 bpp SLCCA outperforms EZW and SPIHT by 1.75 dB and 0.89 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with large texture regions. For eight typical 256 256 grayscale texture images compressed at 0.40 bpp, SLCCA outperforms SPIHT by 0.32 dB{ 0.70 dB. This outstanding performance is achieved without any optimal Bit Allocation Procedure. Thus both the encoding and decoding Procedures are fast
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significance linked connected component analysis for wavelet image coding
IEEE Transactions on Image Processing, 1999Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:The success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's (1993) embedded zerotree wavelets (EZW), Servetto et al.'s (1995) morphological representation of wavelet data (MRWD), and Said and Pearlman's (see IEEE Trans. Circuits Syst. Video Technol., vol.6, p.245-50, 1996) set partitioning in hierarchical trees (SPIHT). We develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, MRWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256/spl times/256 grayscale texture images compressed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This performance is achieved without using any optimal Bit Allocation Procedure. Thus both the encoding and decoding Procedures are fast.
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Significance-linked connected component analysis for wavelet image coding
1999Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:Abstract- Recent success in wavelet image coding is mainly attributed to the recognition of importance of data organization and representation. There have beenseveral very competitive wavelet coders developed, namely, embedded zerotree wavelets (EZW), morphological representation of wavelet data (MRWD), and set partitioning in hierarchical trees (SPIHT). In this paper, we developanovel wavelet image coder called signi cance-linked connected component analysis (SLCCA) of wavelet coe cients that extends MRWD by exploiting both within-subband clustering of signi cant coe cients and cross-subband dependency in significant elds. Computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT as well. For example, for the \Barbara " image, at 0.50 bpp SLCCA outperforms EZW and SPIHT by 1.71 dB and 0.85 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. This outstanding performance is achieved without using any optimal Bit Allocation Procedure. Thus both the encoding and decoding Procedures are fast
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Significance-linked connected component analysis for wavelet image coding
1997Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's embedded zerotree wavelets (EZW), Servetto et al.'s morphological representation of wavelet data (MRWD), and Said and Pearlman's set partitioning in hierarchical trees (SPIHT). In this paper, we develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the \Barbara " image, at 0.5 bpp SLCCA outperforms EZW and SPIHT by 1.75 dB and 0.89 dB in PSNR, respectively. This outstanding performance is achieved without using any optimal Bit Allocation Procedure, thus both the encoding and decoding Procedures are fast
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Significance-linked connected component analysis for wavelet image coding
1997Co-Authors: Bingbing Chai, J Vass, Xinhua ZhuangAbstract:Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's embedded zerotree wavelets (EZW), Servetto et al.'s morphological representation of wavelet data (MRWD), and Said and Pearlman's set partitioning in hierarchical trees (SPIHT). In this paper, we developanovel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the "Barbara" image, at 0.25 bpp SLCCA outperforms EZW, MRWD and SPIHT by 1.41 dB, 0.32 dB and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256 256 grayscale texture images compressed at 0.40 bpp, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This outstanding performance is achieved without using any optimal Bit Allocation Procedure. Thus both the encoding and decoding Procedures are fast
Jean-marie Moureaux - One of the best experts on this subject based on the ideXlab platform.
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Lossy compression of volumetric medical images with 3D dead-zone lattice vector quantization
annals of telecommunications - annales des télécommunications, 2009Co-Authors: Yann Gaudeau, Jean-marie MoureauxAbstract:This paper presents a new lossy coding scheme based on 3D wavelet transform and lattice vector quantization for volumetric medical images. The main contribution of this work is the design of a new codebook enclosing a multidimensional dead zone during the quantization step which enables to better account correlations between neighbor voxels. Furthermore, we present an efficient rate–distortion model to simplify the Bit Allocation Procedure for our intra-band scheme. Our algorithm has been evaluated on several CT- and MR-image volumes. At high compression ratios, we show that it can outperform the best existing methods in terms of rate–distortion trade-off. In addition, our method better preserves details and produces thus reconstructed images less blurred than the well-known 3D SPIHT algorithm which stands as a reference.
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a new fast Bit Allocation Procedure for image coding based on wavelet transform and dead zone lattice vector quantization
International Conference on Image Processing, 2005Co-Authors: L Guillemot, Yann Gaudeau, Jean-marie MoureauxAbstract:In this paper, we present a new Bit Allocation Procedure based on the approximation of the rate distortion (R-D) functions provided by our efficient lattice vector quantizer with pyramidal dead zone (DZLVQ). Here, we show that DZLVQ R-D functions can be efficiently fitted by an exponential model. This property leads to an analytical solution to the Bit Allocation problem which reduces significantly the complexity of our compression scheme. Furthermore, our method is highly parallelizable. Finally we show that it keeps the very good results in terms of visual quality of DZLQV, as it better preserves fine structures with respect to SPIHT and JPEG2000 at low rates.