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Point Cloud Normal Estimation by fast Guided Least Squares Representation

IEEE ACCESS 2020年 8卷 101580-101590页

作者： Zhang, Jie Duan, Jiahui Tang, Kewei Cao, Junjie Liu, Xiuping Liaoning Normal Univ Sch Math Dalian 116029 Peoples R China Dalian Univ Technol Sch Math Sci Dalian 116024 Peoples R China

Normal estimation is an essential task for scanned point clouds in various CAD/CAM applications. The method (GLSRNE) based on guided least squares representation (GLSR) balances speed with quality well among state-of-the-art methods. First, it segments each neighborhood into multiple sub-neighborhoods. For some neighborhoods, the segmentation is obtained by GLSR which is an efficient subspace segmentation model and widely applied in other applications. The segmentation of the rest neighborhoods is inferred via the subspace structure propagation (SSP) algorithm. Then, each sub-neighborhood is fitted by a plane. The plane achieving the minimum distance with the current point is selected for the final normal estimation. We make improvements for effectiveness and efficiency in the following three aspects. First, to improve the speed of GLSR, we propose a novel iterative algorithm to reduce the computation complexity from O(n(3)) to O(n(2)) represents the number of the data points. Moreover, this proposed algorithm will also be useful for other applications. Second, we add a normal constraint for SSP to improve accuracy. Third, when selecting one plane to estimate the final normal, we consider the match between the plane and all neighbors, whereas GLSRNE only considers the match between the plane and the current point. The experiments exhibit that our method is faster than GLSRNE and more effective than GLSRNE and other state-of-the-art methods.

关键词： Estimation Three-dimensional displays Principal component analysis Task analysis Computational complexity Shape Iterative methods Normal estimation feature preserving least squares representation fast algorithm

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DeepSCC: Deep Learning-Based fast Prediction Network for Screen Content Coding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第7期30卷 1917-1932页

作者： Kuang, Wei Chan, Yui-Lam Tsang, Sik-Ho Siu, Wan-Chi Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

Screen content coding (SCC) is an extension of high efficiency video coding (HEVC), and it is developed to improve the coding efficiency of screen content videos by adopting two new coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the flexible quadtree-based coding tree unit (CTU) partitioning structure and various mode candidates make the fast algorithms of the SCC extremely challenging. To efficiently reduce the computational complexity of SCC, we propose a deep learning-based fast prediction network DeepSCC that contains two parts: DeepSCC-I and DeepSCC-II. Before feeding to DeepSCC, incoming coding units (CUs) are divided into two categories: dynamic CTUs and stationary CTUs. For dynamic CTUs having different content as their collocated CTUs, DeepSCC-I takes raw sample values as the input to make fast predictions. For stationary CTUs having the same content as their collocated CTUs, DeepSCC-II additionally utilizes the optimal mode maps of the stationary CTU to further reduce the computational complexity. Compared with the HEVC-SCC reference software SCM-8.3, the proposed DeepSCC reduces the encoding time by 48.81% on average with a negligible Bjontegaard delta bitrate increase of 1.18% under all-intra configuration.

关键词： Encoding Copper Partitioning algorithms High efficiency video coding Deep learning Videos Computational complexity Screen content coding (SCC) high efficiency video coding (HEVC) fast algorithm convolutional neural network (CNN) deep learning

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fast Implementation of Signal Reconstruction based on Sparse Bayesian Learning with Fourier Dictionary

Fast Implementation of Signal Reconstruction based on Sparse...

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2021 CIE International Conference on Radar, Radar 2021

作者： Wang, Yuanyuan Jin, Shuojing Dai, Fengzhou Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9781665498142

Sparse Bayesian learning (SBL) is a robust algorithm for sparse signal reconstruction (SSR) and widely applied in many fields. Unfortunately, the conventional SBL algorithm suffers from heavy computational complexity due to the matrix inversion involved in each iteration, which is proportional to the cube of the observed data length and thus prevents it solving large-scale data. In this paper, based on the Fourier Dictionary (FD)-based SBL, abbreviated as FD-SBL, we propose an efficient FD-SBL method to implement SSR for uniformly sampled one-dimensional (1D) case. By utilizing the property that the matrix to be inverted is a Toeplitz matrix, we can exploit the Gohberg-Semencul (G-S)-type factorization to reduce the computational complexity of the FD-SBL algorithm. To be noted, there is no approximation in our proposed method, and the computational cost is reduced by several orders of magnitude compared with the conventional SBL algorithm. Finally, the experimental results verify the effectiveness of the proposed method. © 2021 IEEE.

关键词： fast algorithm Fourier dictionary Gohberg-Semencul Factorization Sparse Bayesian learning

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A class of efficient time-stepping methods for multi-term time-fractional reaction-diffusion-wave equations

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APPLIED NUMERICAL MATHEMATICS 2021年 165卷 56-82页

作者： Yin, Baoli Liu, Yang Li, Hong Zeng, Fanhai Inner Mongolia Univ Sch Math Sci Hohhot 010021 Peoples R China Shandong Univ Sch Math Jinan 250100 Peoples R China

A family of novel time-stepping methods for the fractional calculus operators is presented with a shifted parameter. The truncation error with second-order accuracy is proved under the framework of the shifted convolution quadrature. To improve the efficiency, two aspects are considered, that i) a fast algorithm is developed to reduce the computation complexity from O(N-t(2)) to O(N-t log N-t) and the memory requirement from O(N-t) to O(log N-t), where N-t denotes the number of successive time steps, and ii) correction terms are added to deal with the initial singularity of the solution. The stability analysis and error estimates are provided in detail where in temporal direction the novel time-stepping methods are applied and the spatial variable is discretized by the finite element method. Numerical results for d-dimensional examples (d = 1, 2, 3) confirm our theoretical conclusions and the efficiency of the fast algorithm. (C) 2021 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Fractional reaction-diffusion-wave equation Shifted convolution quadrature fast algorithm Correction terms Time-stepping methods

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fast intra prediction decision algorithm for the QTBT structure

Fast intra prediction decision algorithm for the QTBT struct...

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IEEE International Conference on Design and Test of Integrated Micro and Nano-Systems (DTS)

作者： Zouidi, Naima Belghith, Fatma Kessentini, Amina Masmoudi, Nouri Univ Sfax Natl Sch Engineers Elect & Informat Technol Lab Sfax Tunisia

ISBN: (纸本)9781728111285

The Joint Exploration Team JVET, created in October 2015, between ITU-T and ISO, is currently preparing the next video coding standard called Versatile Video Coding (VVC). This proposition, which is expected to be finalized by the end of 2020, is designed to address high-resolution 8k video applications and emerging applications such as 360-degree omnidirectional and High Dynamic Range (HDR) videos. The new proposition comes with different improvements, especially for the intra prediction module. An increased set of angular modes IPM (Intra Prediction Modes) was proposed to model angular textures more accurately. In addition, a new partition structure called Quadtree plus Binary Tree (QTBT) replaced the quadtree structure. These enhancements aiming at increasing the coding efficiency have led to burden coding complexity particularly in terms of coding time. In this paper, a fast intra prediction mode decision algorithm was proposed for the different QTBT binary depths. Experimental results show the effectiveness of the proposed method, which allows a complexity reduction of 56.32% with a negligible loss in video quality of -0.02 dB on average and a bite rate increase between 1% and 3%.

关键词： VVC Intra prediction RD optimization fast algorithm Complexity reduction

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Acceleration of the Computation of the Method of Moments EFIE Impedance Matrix From an Updated Fraunhofer Criterion

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2021年第5期69卷 3039-3043页

作者： Bourlier, Christophe Univ Nantes CNRS Inst Elect & Technol NumeR IETR LabUMR 6164 F-44035 Nantes France

This communication deals with the acceleration of the computation of the impedance matrix obtained from the electric field integral equation (EFIE) discretized by the Galerkin method of moments (MoM) with Rao-Wilton-Glisson basis functions. The elements of the impedance matrix need to calculate a double integral (quadruple integral) over two planar triangles, which is typically done from two numerical Gauss-Legendre integrations. For far-field interactions, this integration can be done analytically by introducing a criterion for which the resulting closed-form expression is valid. This approximation is tested on a sphere and a concave cavity-shaped object, for which the results show that the time saving factor is about 20, with a mean difference of 0.1-0.5 dB on the radar cross section (RCS) compared to that obtained from two numerical integrations.

关键词： Method of moments Impedance Transmission line matrix methods Antennas Integral equations Scattering Acceleration Electric field integral equation (EFIE) fast algorithm method of moments (MoM) radar cross section (RCS)

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fast intra coding unit partition decision in H.266/FVC based on spatial features

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2020年第3期17卷 493-510页

作者： Lin, Ting-Lan Jiang, Hui-Yu Huang, Jing-Ya Chang, Pao-Chi Natl Taipei Univ Technol Dept Elect Engn Taipei Taiwan Natl Cent Univ Dept Commun Engn Video Audio Proc Lab VAPLab Taoyuan Taiwan Natl Cent Univ Dept Commun Engn Taoyuan Taiwan

With the development of technology, the requirements of hardware equipment and user expectations of visual enjoyment are increasingly gradually. The Joint Video Exploration Team (JVET) has established the latest video compression standard, Future Video Coding (FVC). FVC adopts QuadTree plus Binary Tree (QTBT) based Coding Unit (CU) structure, which not only removes the complex hierarchical structure of the CU, Prediction Unit (PU), and Transform Unit (TU) but also supports square and rectangular coding blocks based on the texture of the video content. Although the QTBT structure can provide superior coding performance, it significantly increases the encoding time, particularly in intra coding. Therefore, developing a fast intra CU partition decision algorithm is essential. In this paper, a fast CU partition decision algorithm in FVC intra coding based on spatial features is proposed. Different spatial features in the pixel domain are proposed in the binary tree and quadtree decision processes. Spatial features for the binary tree are employed for early skipping of the encoding process of CUs with binary tree depth and for early determination of binary tree split mode. Spatial features for the quadtree are employed for early splitting or termination of CUs with quadtree depth. Compared with JEM 5.0, the proposed method can save 23% encoding time on average with a slight increase of 0.62% in the Bjontegaard delta bitrate (BDBR).

关键词： H 266 future video coding (FVC) fast algorithm Intra coding Spatial feature Quadtree plus binary tree (QTBT)

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fast depth intra coding based on spatial correlation and rate distortion cost in 3D-HEVC

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2020年 80卷 115668-000页

作者： Li, Tiansong Wang, Hongkui Chen, Yamei Yu, Li Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China

3D-HEVC video coding standard adopts many sophisticated algorithms to improve the coding performance, which results in heavy calculation complexity. To reduce the computation burden, a complexity reduction scheme for depth map based on spatial correlation and the rate-distortion (RD) cost is proposed. Firstly, A maximum depth layer decision (MDLD) algorithm is proposed to predict the maximum depth layer (MDL) of each coding tree unit (CTU) accurately. Using the frequency distribution histogram theory in the statistics, the frequency distribution characteristics (FDC) of the RDcost J(0) of the CTU level in depth layer 0 is investigated under different MDL The initial threshold for different MDL is designed based on the FDC of the J(0) under allowable false rate (FR). An exponential relationship between the threshold and QP is discovered. Secondly, A fast depth intra mode decision (FDIMD) algorithm is proposed to skip unnecessary prediction modes. Considering the spatial correlation, the FDC of the RDcost J(ORM) of the optimal rough mode (ORM) in rough mode decision (RMD) is analyzed under different optimal prediction mode. Based on the FDC of the J(ORM) the mode-skipping threshold T H-st is designed to skip angular modes and depth modeling modes (DMMs). An exponential model is built to represent the relationship between the T H-st and QP. Finally, a fast wedgelet pattern decision method based on K-Means is developed to reduce the complexity of DMM1. Experimental results show that the proposed algorithm achieves an average reduction of 58.9% on intra coding time, with negligible degradation of coding performance. The proposed algorithm performs better in terms of complexity and performance than other approaches.

关键词： 3D-HEVC Depth infra coding Depth map fast algorithm Spatial correlation

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fast EVALUATION OF ARTIFICIAL BOUNDARY CONDITIONS FOR ADVECTION DIFFUSION EQUATIONS

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SIAM JOURNAL ON NUMERICAL ANALYSIS 2020年第6期58卷 3530-3557页

作者： Sun, Ting Wang, Jilu Zheng, Chunxiong Xinjiang Univ Coll Math & Syst Sci Urumqi 830046 Peoples R China Beijing Computat Sci Res Ctr Beijing 100193 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

An artificial boundary method is developed for solving the one-dimensional advection diffusion equation in the real line. In order to construct a fully discrete fast numerical algorithm with rigorous error analysis, we start with the two-step backward difference formula for time discretization of the advection diffusion equation in the whole real line. Then, we use the discrete analogue of the Laplace transform to derive a second-order time-stepping scheme in a bounded domain equipped with a discrete artificial boundary condition (ABC). The Galerkin finite element method is used for spatial discretization. To expedite the evaluation of time convolution involved in the discrete ABC, we propose a fast algorithm based on the best rational approximation of square root function in subdomains of the complex plane. An estimate for this best rational approximation enables us to prove optimal-order convergence of the fully discrete numerical scheme (integrating the fast approximation algorithm). Several numerical examples are provided to illustrate the convergence of numerical solutions and the effectiveness of the proposed fast approximation algorithm.

关键词： artificial boundary method fast algorithm advection diffusion equation rational approximation error estimates

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Efficient Implementation for SBL-Based Coherent Distributed mmWave Radar Imaging

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REMOTE SENSING 2023年第4期15卷 1054-1054页

作者： Dai, Fengzhou Li, Yuhang Wang, Yuanyuan Chen, Hao Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China Infineon Technol Ctr Competence Shanghai Co Ltd Shanghai 201203 Peoples R China

In a distributed frequency-modulated continuous waveform (FMCW) radar system, the echo data collected are not continuous in the azimuth direction, so the imaging effect of the traditional range-Doppler (RD) algorithm is poor. Sparse Bayesian learning (SBL) is an optimization algorithm based on Bayesian theory that has been successfully applied to high-resolution radar imaging because of its strong robustness and high accuracy. However, SBL is highly computationally complex. Fortunately, with FMCW radar echo data, most of the time-consuming SBL operations involve a Toeplitz-block Toeplitz (TBT) matrix. In this article, based on this advantage, we propose a fast SBL algorithm that can be used to obtain high-angular-resolution images, in which the inverse of the TBT matrix can be transposed as the sum of the products of the block lower triangular Toeplitz matrix and the block circulant matrix by using a new decomposition method, and some of the matrix multiplications can be quickly computed using the fast Fourier transform (FFT), decreasing the computation time by several orders of magnitude. Finally, simulations and experiments were used to ensure the effectiveness of the proposed algorithm.

关键词： distributed multiple-input multiple-output (MIMO) radar system frequency -modulated continuous waveform (FMCW) radar imaging sparse Bayesian learning fast algorithm compressed sensing

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