The Experts below are selected from a list of 7821 Experts worldwide ranked by ideXlab platform
Niklas Grip - One of the best experts on this subject based on the ideXlab platform.
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Efficient wavelet prefilters with optimal time-shifts
IEEE Transactions on Signal Processing, 2005Co-Authors: Stefan Ericsson, Niklas GripAbstract:A wavelet prefilter maps Sample values of an analyzed signal to the scaling function coefficient input of standard discrete wavelet transform (DWT) algorithms. The prefilter is the inverse of a certain postfilter convolution matrix consisting of Integer Sample values of a nonInteger-shifted wavelet scaling function. For the prefilter and the DWT algorithms to have similar computational complexity, it is often necessary to use a "short enough" approximation of the prefilter. In addition to well-known quadrature formula and identity matrix prefilter approximations, we propose a Neumann series approximation, which is a band matrix truncation of the optimal prefilter, and derive simple formulas for the operator norm approximation error. This error shows a dramatic dependence on how the postfilter nonInteger shift is chosen. We explain the meaning of this shift in practical applications, describe how to choose it, and plot optimally shifted prefilter approximation errors for 95 different Daubechies, Symlet, and B-spline wavelets. Whereas the truncated inverse is overall superior, the Neumann filters are by far the easiest ones to compute, and for some short support wavelets, they also give the smallest approximation error. For example, for Daubechies 1-5 wavelets, the simplest Neumann prefilter provide an approximation error reduction corresponding to 100-10 000 times oversampling in a nonprefiltered system.
Stefan Ericsson - One of the best experts on this subject based on the ideXlab platform.
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Efficient wavelet prefilters with optimal time-shifts
IEEE Transactions on Signal Processing, 2005Co-Authors: Stefan Ericsson, Niklas GripAbstract:A wavelet prefilter maps Sample values of an analyzed signal to the scaling function coefficient input of standard discrete wavelet transform (DWT) algorithms. The prefilter is the inverse of a certain postfilter convolution matrix consisting of Integer Sample values of a nonInteger-shifted wavelet scaling function. For the prefilter and the DWT algorithms to have similar computational complexity, it is often necessary to use a "short enough" approximation of the prefilter. In addition to well-known quadrature formula and identity matrix prefilter approximations, we propose a Neumann series approximation, which is a band matrix truncation of the optimal prefilter, and derive simple formulas for the operator norm approximation error. This error shows a dramatic dependence on how the postfilter nonInteger shift is chosen. We explain the meaning of this shift in practical applications, describe how to choose it, and plot optimally shifted prefilter approximation errors for 95 different Daubechies, Symlet, and B-spline wavelets. Whereas the truncated inverse is overall superior, the Neumann filters are by far the easiest ones to compute, and for some short support wavelets, they also give the smallest approximation error. For example, for Daubechies 1-5 wavelets, the simplest Neumann prefilter provide an approximation error reduction corresponding to 100-10 000 times oversampling in a nonprefiltered system.
Thomas Wiegand - One of the best experts on this subject based on the ideXlab platform.
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Pattern-based Integer Sample motion search strategies in the context of HEVC
Applications of Digital Image Processing XXXVIII, 2015Co-Authors: Georg Maier, Benjamin Bross, Dan Grois, Detlev Marpe, Heiko Schwarz, Remco C. Veltkamp, Thomas WiegandAbstract:The H.265/MPEG-H High Efficiency Video Coding (HEVC) standard provides a significant increase in coding efficiency compared to its predecessor, the H.264/MPEG-4 Advanced Video Coding (AVC) standard, which however comes at the cost of a high computational burden for a compliant encoder. Motion estimation (ME), which is a part of the inter-picture prediction process, typically consumes a high amount of computational resources, while significantly increasing the coding efficiency. In spite of the fact that both H.265/MPEG-H HEVC and H.264/MPEG-4 AVC standards allow processing motion information on a fractional Sample level, the motion search algorithms based on the Integer Sample level remain to be an integral part of ME. In this paper, a flexible Integer Sample ME framework is proposed, thereby allowing to trade off significant reduction of ME computation time versus coding efficiency penalty in terms of bit rate overhead. As a result, through extensive experimentation, an Integer Sample ME algorithm that provides a good trade-off is derived, incorporating a combination and optimization of known predictive, pattern-based and early termination techniques. The proposed ME framework is implemented on a basis of the HEVC Test Model (HM) reference software, further being compared to the state-of-the-art fast search algorithm, which is a native part of HM. It is observed that for high resolution sequences, the Integer Sample ME process can be speed-up by factors varying from 3.2 to 7.6, resulting in the bit-rate overhead of 1.5% and 0.6% for Random Access (RA) and Low Delay P (LDP) configurations, respectively. In addition, the similar speed-up is observed for sequences with mainly Computer-Generated Imagery (CGI) content while trading off the bit rate overhead of up to 5.2%.
Georg Maier - One of the best experts on this subject based on the ideXlab platform.
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Pattern-based Integer Sample motion search strategies in the context of HEVC
Applications of Digital Image Processing XXXVIII, 2015Co-Authors: Georg Maier, Benjamin Bross, Dan Grois, Detlev Marpe, Heiko Schwarz, Remco C. Veltkamp, Thomas WiegandAbstract:The H.265/MPEG-H High Efficiency Video Coding (HEVC) standard provides a significant increase in coding efficiency compared to its predecessor, the H.264/MPEG-4 Advanced Video Coding (AVC) standard, which however comes at the cost of a high computational burden for a compliant encoder. Motion estimation (ME), which is a part of the inter-picture prediction process, typically consumes a high amount of computational resources, while significantly increasing the coding efficiency. In spite of the fact that both H.265/MPEG-H HEVC and H.264/MPEG-4 AVC standards allow processing motion information on a fractional Sample level, the motion search algorithms based on the Integer Sample level remain to be an integral part of ME. In this paper, a flexible Integer Sample ME framework is proposed, thereby allowing to trade off significant reduction of ME computation time versus coding efficiency penalty in terms of bit rate overhead. As a result, through extensive experimentation, an Integer Sample ME algorithm that provides a good trade-off is derived, incorporating a combination and optimization of known predictive, pattern-based and early termination techniques. The proposed ME framework is implemented on a basis of the HEVC Test Model (HM) reference software, further being compared to the state-of-the-art fast search algorithm, which is a native part of HM. It is observed that for high resolution sequences, the Integer Sample ME process can be speed-up by factors varying from 3.2 to 7.6, resulting in the bit-rate overhead of 1.5% and 0.6% for Random Access (RA) and Low Delay P (LDP) configurations, respectively. In addition, the similar speed-up is observed for sequences with mainly Computer-Generated Imagery (CGI) content while trading off the bit rate overhead of up to 5.2%.
Benjamin Bross - One of the best experts on this subject based on the ideXlab platform.
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Pattern-based Integer Sample motion search strategies in the context of HEVC
Applications of Digital Image Processing XXXVIII, 2015Co-Authors: Georg Maier, Benjamin Bross, Dan Grois, Detlev Marpe, Heiko Schwarz, Remco C. Veltkamp, Thomas WiegandAbstract:The H.265/MPEG-H High Efficiency Video Coding (HEVC) standard provides a significant increase in coding efficiency compared to its predecessor, the H.264/MPEG-4 Advanced Video Coding (AVC) standard, which however comes at the cost of a high computational burden for a compliant encoder. Motion estimation (ME), which is a part of the inter-picture prediction process, typically consumes a high amount of computational resources, while significantly increasing the coding efficiency. In spite of the fact that both H.265/MPEG-H HEVC and H.264/MPEG-4 AVC standards allow processing motion information on a fractional Sample level, the motion search algorithms based on the Integer Sample level remain to be an integral part of ME. In this paper, a flexible Integer Sample ME framework is proposed, thereby allowing to trade off significant reduction of ME computation time versus coding efficiency penalty in terms of bit rate overhead. As a result, through extensive experimentation, an Integer Sample ME algorithm that provides a good trade-off is derived, incorporating a combination and optimization of known predictive, pattern-based and early termination techniques. The proposed ME framework is implemented on a basis of the HEVC Test Model (HM) reference software, further being compared to the state-of-the-art fast search algorithm, which is a native part of HM. It is observed that for high resolution sequences, the Integer Sample ME process can be speed-up by factors varying from 3.2 to 7.6, resulting in the bit-rate overhead of 1.5% and 0.6% for Random Access (RA) and Low Delay P (LDP) configurations, respectively. In addition, the similar speed-up is observed for sequences with mainly Computer-Generated Imagery (CGI) content while trading off the bit rate overhead of up to 5.2%.