The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
W.k. Cham - One of the best experts on this subject based on the ideXlab platform.
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LLM Integer Cosine Transform and its Fast Algorithm
IEEE Transactions on Circuits and Systems for Video Technology, 2012Co-Authors: Chi-keung Fong, W.k. ChamAbstract:Existing video coding standards use only 4 × 4 and 8 × 8 Transforms for energy compaction. Recent research has found that the use of larger Transforms, such as 16 × 16, together with the existing Transforms can improve coding performance especially in high-definition (HD) videos which are becoming more and more common. This raises the interest of seeking high-performance higher-order Transforms with low computation requirement. In this paper, a method to derive orthogonal integer Cosine Transforms is proposed. The order-2N Transform is defined using the order-N Transform. A family of these integer Transforms, Loeffler, Ligtenberg, and Moschytz (LLM) integer Cosine Transform, is derived using this method. Its fast algorithm structure is the same as LLM fast discrete Cosine Transform (DCT) algorithm but requires integer operations only. This new family of Transforms is not only very close to the DCT but also has excellent coding performance.
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A Modified Cosine Transform
Journal of Visual Communication and Image Representation, 1993Co-Authors: W.k. ChamAbstract:In this work, we propose to improve the performance of the DCT by weighting the DCT matrices with some simple structured orthonormal matrices. The new Transform thus generated is called the weighted Cosine Transform (WCT). This method of modification ensures that fast computational algorithms exist for the WCT. Tests using both a statistical model and real images have shown that the WCT performs better than the DCT. The implementation of an order-N WCT is similar to that of the DCT. The additional computation overhead is 1.5N multiplications and N additions.
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An order-16 integer Cosine Transform
IEEE Transactions on Signal Processing, 1991Co-Authors: W.k. Cham, Y.t. ChanAbstract:It is shown that it is possible to replace the real-numbered elements of a discrete Cosine Transform (DCT) matrix with integers and still maintain the structure, i.e., relative magnitudes and orthogonality, among the matrix elements. The result is an integer Cosine Transform (ICT). Thirteen ICTs have been found, and some of them have performance comparable to the DCT. The main advantage of the ICT lies in having only integer values, which in two cases can be represented perfectly by 6-bit numbers, thus providing a potential reduction in the computational complexity. >
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Image coding using weighted Cosine Transform
IEEE TENCON'90: 1990 IEEE Region 10 Conference on Computer and Communication Systems. Conference Proceedings, 1Co-Authors: W.k. ChamAbstract:A new orthogonal Transform, called weighted Cosine Transform (WCT), is developed for digital image coding applications. The new Transform is characterized. The Transform matrix is the weighted version of that in the discrete Cosine Transform (DCT). Various commonly used criteria based on the one-dimensional and two-dimensional Markov models have shown that the performance of the order-8 and order-16 WCT is better than the DCT and the phase-shift Cosine Transform (PSCT), which is an improved version of the DCT. A fast computational algorithm for the WCT is also derived, which, however, requires more computational efforts than the DCT. >
Kaoru Sezaki - One of the best experts on this subject based on the ideXlab platform.
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reversible discrete Cosine Transform
International Conference on Acoustics Speech and Signal Processing, 1998Co-Authors: Kunitoshi Komatsu, Kaoru SezakiAbstract:In this paper a reversible discrete Cosine Transform (RDCT) is presented. The N-point reversible Transform is firstly presented, then the 8-point RDCT is obtained by substituting the 2 and 4-point reversible Transforms for the 2 and 4-point Transforms which compose the 8-point discrete Cosine Transform (DCT), respectively. The integer input signal can be losslessly recovered, although the Transform coefficients are integer numbers. If the floor functions are ignored in RDCT, the Transform is exactly the same as DCT with determinant=1. RDCT is also normalized so that we can avoid the problem that dynamic range is nonuniform. A simulation on continuous-tone still images shows that the lossless and lossy compression efficiencies of RDCT are comparable to those obtained with reversible wavelet Transform.
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ICASSP - Reversible discrete Cosine Transform
Proceedings of the 1998 IEEE International Conference on Acoustics Speech and Signal Processing ICASSP '98 (Cat. No.98CH36181), 1Co-Authors: Kunitoshi Komatsu, Kaoru SezakiAbstract:In this paper a reversible discrete Cosine Transform (RDCT) is presented. The N-point reversible Transform is firstly presented, then the 8-point RDCT is obtained by substituting the 2 and 4-point reversible Transforms for the 2 and 4-point Transforms which compose the 8-point discrete Cosine Transform (DCT), respectively. The integer input signal can be losslessly recovered, although the Transform coefficients are integer numbers. If the floor functions are ignored in RDCT, the Transform is exactly the same as DCT with determinant=1. RDCT is also normalized so that we can avoid the problem that dynamic range is nonuniform. A simulation on continuous-tone still images shows that the lossless and lossy compression efficiencies of RDCT are comparable to those obtained with reversible wavelet Transform.
Kunitoshi Komatsu - One of the best experts on this subject based on the ideXlab platform.
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reversible discrete Cosine Transform
International Conference on Acoustics Speech and Signal Processing, 1998Co-Authors: Kunitoshi Komatsu, Kaoru SezakiAbstract:In this paper a reversible discrete Cosine Transform (RDCT) is presented. The N-point reversible Transform is firstly presented, then the 8-point RDCT is obtained by substituting the 2 and 4-point reversible Transforms for the 2 and 4-point Transforms which compose the 8-point discrete Cosine Transform (DCT), respectively. The integer input signal can be losslessly recovered, although the Transform coefficients are integer numbers. If the floor functions are ignored in RDCT, the Transform is exactly the same as DCT with determinant=1. RDCT is also normalized so that we can avoid the problem that dynamic range is nonuniform. A simulation on continuous-tone still images shows that the lossless and lossy compression efficiencies of RDCT are comparable to those obtained with reversible wavelet Transform.
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ICASSP - Reversible discrete Cosine Transform
Proceedings of the 1998 IEEE International Conference on Acoustics Speech and Signal Processing ICASSP '98 (Cat. No.98CH36181), 1Co-Authors: Kunitoshi Komatsu, Kaoru SezakiAbstract:In this paper a reversible discrete Cosine Transform (RDCT) is presented. The N-point reversible Transform is firstly presented, then the 8-point RDCT is obtained by substituting the 2 and 4-point reversible Transforms for the 2 and 4-point Transforms which compose the 8-point discrete Cosine Transform (DCT), respectively. The integer input signal can be losslessly recovered, although the Transform coefficients are integer numbers. If the floor functions are ignored in RDCT, the Transform is exactly the same as DCT with determinant=1. RDCT is also normalized so that we can avoid the problem that dynamic range is nonuniform. A simulation on continuous-tone still images shows that the lossless and lossy compression efficiencies of RDCT are comparable to those obtained with reversible wavelet Transform.
Knut Huper - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - The Discrete Cosine Transform on Triangles
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Bastian Seifert, Knut HuperAbstract:The discrete Cosine Transform is a valuable tool in analysis of data on undirected rectangular grids, like images. In this paper it is shown how one can define an analogue of the discrete Cosine Transform on triangles. This is done by combining algebraic signal processing theory with a specific kind of multivariate Chebyshev polynomials. Using a multivariate Christoffel-Darboux formula it is shown how to derive an orthogonal version of the Transform.
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The discrete Cosine Transform on triangles
arXiv: Numerical Analysis, 2018Co-Authors: Bastian Seifert, Knut HuperAbstract:The discrete Cosine Transform is a valuable tool in analysis of data on undirected rectangular grids, like images. In this paper it is shown how one can define an analogue of the discrete Cosine Transform on triangles. This is done by combining algebraic signal processing theory with a specific kind of multivariate Chebyshev polynomials. Using a multivariate Christoffel-Darboux formula it is shown how to derive an orthogonal version of the Transform.
L Westover - One of the best experts on this subject based on the ideXlab platform.
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a forward mapping realization of the inverse discrete Cosine Transform
Data Compression Conference, 1992Co-Authors: Leonard Mcmillan, L WestoverAbstract:The paper presents a new realization of the inverse discrete Cosine Transform (IDCT). It exploits both the decorrelation properties of the discrete Cosine Transform (DCT) and the quantization process that is frequently applied to the DCT's resultant coefficients. This formulation has several advantages over previous approaches, including the elimination of multiplies from the central loop of the algorithm and its adaptability to incremental evaluation. The technique provides a significant reduction in computational requirements of the IDCT, enabling a software-based implementation to perform at rates which were previously achievable only through dedicated hardware. >
