The Experts below are selected from a list of 12468 Experts worldwide ranked by ideXlab platform
J W Havlicek - One of the best experts on this subject based on the ideXlab platform.
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coperm transform domain Energy Compaction by optimal permutation
IEEE Transactions on Signal Processing, 1999Co-Authors: N D Sidiropoulos, Marios S Pattichis, Alan C Bovik, J W HavlicekAbstract:Compaction by optimal permutation (COPERM) is a tool for transform domain Energy Compaction of broadband signals, whose foundation is a simple but powerful idea: any signal can be transformed to resemble a more desirable (e.g., from a transform-domain Compaction viewpoint) signal from a class of "target" signals (e.g., DCT basis functions) by means of a suitable permutation of its samples. One application of transform-domain Energy Compaction is in lossy compression. We pursue one possible thread in detail and demonstrate some interesting broadband image compression results.
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coperm transform domain Energy Compaction by optimal permutation
International Conference on Acoustics Speech and Signal Processing, 1998Co-Authors: N D Sidiropoulos, Marios S Pattichis, Alan C Bovik, J W HavlicekAbstract:COPERM is a novel paradigm for Energy Compaction and signal compression, whose foundation is a simple but powerful idea: any signal can be transformed to resemble a more desirable signal from a class of "target" signals, by means of a suitable permutation of its samples. The approach is well-suited for transform domain Energy Compaction prior to transform-domain compression of persistent broadband signals. The associated optimal permutation precoders are surprisingly simple, and the permutation precoding overhead can be made modest-resulting in improved overall rate-distortion performance.
N D Sidiropoulos - One of the best experts on this subject based on the ideXlab platform.
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coperm transform domain Energy Compaction by optimal permutation
IEEE Transactions on Signal Processing, 1999Co-Authors: N D Sidiropoulos, Marios S Pattichis, Alan C Bovik, J W HavlicekAbstract:Compaction by optimal permutation (COPERM) is a tool for transform domain Energy Compaction of broadband signals, whose foundation is a simple but powerful idea: any signal can be transformed to resemble a more desirable (e.g., from a transform-domain Compaction viewpoint) signal from a class of "target" signals (e.g., DCT basis functions) by means of a suitable permutation of its samples. One application of transform-domain Energy Compaction is in lossy compression. We pursue one possible thread in detail and demonstrate some interesting broadband image compression results.
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coperm transform domain Energy Compaction by optimal permutation
International Conference on Acoustics Speech and Signal Processing, 1998Co-Authors: N D Sidiropoulos, Marios S Pattichis, Alan C Bovik, J W HavlicekAbstract:COPERM is a novel paradigm for Energy Compaction and signal compression, whose foundation is a simple but powerful idea: any signal can be transformed to resemble a more desirable signal from a class of "target" signals, by means of a suitable permutation of its samples. The approach is well-suited for transform domain Energy Compaction prior to transform-domain compression of persistent broadband signals. The associated optimal permutation precoders are surprisingly simple, and the permutation precoding overhead can be made modest-resulting in improved overall rate-distortion performance.
Xianggen Xia - One of the best experts on this subject based on the ideXlab platform.
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a new prefilter design for discrete multiwavelet transforms
IEEE Transactions on Signal Processing, 1998Co-Authors: Xianggen XiaAbstract:In conventional wavelet transforms, prefiltering is not necessary due to the lowpass property of a scaling function. This is no longer true for multiwavelet transforms. A few research papers on the design of prefilters have appeared, but the existing prefilters are usually not orthogonal, which often causes problems in coding. Moreover, the condition on the prefilters was imposed based on the first-step discrete multiwavelet decomposition. We propose a new prefilter design that combines the ideas of the conventional wavelet transforms and multiwavelet transforms. The prefilters are orthogonal but nonmaximally decimated. They are derived from a very natural calculation of multiwavelet transform coefficients. In this new prefilter design, multiple step discrete multiwavelet decomposition is taken into account. Our numerical examples (by taking care of the redundant prefiltering) indicate that the Energy Compaction ratio with the Geronimo-Hardin-Massopust (1994) 2 wavelet transform and our new prefiltering is better than the one with Daubechies D/sub 4/ wavelet transform.
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a new prefilter design for discrete multiwavelet transforms
Asilomar Conference on Signals Systems and Computers, 1997Co-Authors: Xianggen XiaAbstract:We propose a new prefilter design that combines the ideas of the conventional wavelet transforms and multiwavelet transforms. The prefilters are orthogonal but nonmaximally decimated. They are derived from a very natural calculation of multiwavelet transform coefficients. In this new prefilter design, multiple step discrete multiwavelet decomposition is taken into account. Our numerical examples (by taking care of the redundant prefiltering) indicate that the Energy Compaction ratio with the Geronimo, Hardin and Massopust (1994) 2 wavelet transform and our new prefiltering is better than the one with Daubechies D/sub 4/ wavelet transform.
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design of prefilters for discrete multiwavelet transforms
IEEE Transactions on Signal Processing, 1996Co-Authors: Xianggen Xia, Douglas P. Hardin, Jeffrey S Geronimo, Bruce W SuterAbstract:The pyramid algorithm for computing single wavelet transform coefficients is well known. The pyramid algorithm can be implemented by using tree-structured multirate filter banks. The authors propose a general algorithm to compute multiwavelet transform coefficients by adding proper premultirate filter banks before the vector filter banks that generate multiwavelets. The proposed algorithm can be thought of as a discrete vector-valued wavelet transform for certain discrete-time vector-valued signals. The proposed algorithm can be also thought of as a discrete multiwavelet transform for discrete-time signals. The authors then present some numerical experiments to illustrate the performance of the algorithm, which indicates that the Energy Compaction for discrete multiwavelet transforms may be better than the one for conventional discrete wavelet transforms.
K R Rao - One of the best experts on this subject based on the ideXlab platform.
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image coding based on classified lapped orthogonal transform vector quantization
IEEE Transactions on Circuits and Systems for Video Technology, 1995Co-Authors: S Verkatraman, J Y Nam, K R RaoAbstract:Classified transform coding of images using vector quantization (VQ) has proved to be an efficient technique. Transform VQ combines the Energy Compaction properties of transform coding and the superior performance of VQ. Classification improves the reconstructed image quality considerably because of adaptive bit allocation. A classified transform VQ technique using the lapped orthogonal transform (LOT) is presented. Image blocks are transformed using the LOT and are classified into four classes based on their structural properties. These are further divided adaptively into subvectors depending on the LOT coefficient statistics as this allows efficient distribution of bits. These subvectors are then vector quantized. Simulation results indicate subjectively improved images with LOT/VQ as compared to DCT/VQ. >
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image coding based on classified lapped orthogonal transform vector quantization
Visual Communications and Image Processing, 1992Co-Authors: Suresh Venkatraman, J Y Nam, K R RaoAbstract:Classified transform coding of images using vector quantization has proved to be an efficient technique. Transform vector quantization combines the Energy Compaction properties of transform coding and the superior performance of vector quantization. Classification improves the reconstructed image quality considerably because of adaptive bit allocation. Block transform coding of images, traditionally using DCT, produces an undesirable effect called the blocking effect. In this paper a classified transform vector quantization technique using the lapped orthogonal transform (LOT/VQ) is presented. Image blocks are transformed using the LOT and are classified into four classes based on their structural properties. These are further divided adaptively into subvectors depending on the LOT coefficient statistics as this allows efficient distribution of bits. These subvectors are then vector quantized. The LOT/VQ is an efficient image coding algorithm which also reduces the blocking effect significantly. Coding tests using computer simulation show the effectiveness of this technique.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Alan C Bovik - One of the best experts on this subject based on the ideXlab platform.
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coperm transform domain Energy Compaction by optimal permutation
IEEE Transactions on Signal Processing, 1999Co-Authors: N D Sidiropoulos, Marios S Pattichis, Alan C Bovik, J W HavlicekAbstract:Compaction by optimal permutation (COPERM) is a tool for transform domain Energy Compaction of broadband signals, whose foundation is a simple but powerful idea: any signal can be transformed to resemble a more desirable (e.g., from a transform-domain Compaction viewpoint) signal from a class of "target" signals (e.g., DCT basis functions) by means of a suitable permutation of its samples. One application of transform-domain Energy Compaction is in lossy compression. We pursue one possible thread in detail and demonstrate some interesting broadband image compression results.
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coperm transform domain Energy Compaction by optimal permutation
International Conference on Acoustics Speech and Signal Processing, 1998Co-Authors: N D Sidiropoulos, Marios S Pattichis, Alan C Bovik, J W HavlicekAbstract:COPERM is a novel paradigm for Energy Compaction and signal compression, whose foundation is a simple but powerful idea: any signal can be transformed to resemble a more desirable signal from a class of "target" signals, by means of a suitable permutation of its samples. The approach is well-suited for transform domain Energy Compaction prior to transform-domain compression of persistent broadband signals. The associated optimal permutation precoders are surprisingly simple, and the permutation precoding overhead can be made modest-resulting in improved overall rate-distortion performance.