With the development of multimedia presentation technology,image acquisition technology and the Internet industry,long-distance communication methods have changed from the previous letter,the audio to the current audi...With the development of multimedia presentation technology,image acquisition technology and the Internet industry,long-distance communication methods have changed from the previous letter,the audio to the current audio/video.And the proportion of video in work,study and entertainment keeps increasing,high-definition video is getting more and more attention.Due to the limits of the network environment and storage capacity,the original video must be encoded to be efficiently transmitted and stored.High Efficient Video Coding(HEVC)requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process.The optimal quantization parameter is calculated by comparing the rate distortion cost.In this paper,we propose a fast decision method for HEVC quantization parameters selection based on convolutional neural network,which saves video’s encoding time.展开更多
The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth...The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/formats.This paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy levels.The quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity reduction.Further,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths.The same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is achieved.Using the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats.展开更多
This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-thresho...This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.展开更多
In this paper,an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization,inertial uncertainty and external disturbance.TThe control signal f...In this paper,an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization,inertial uncertainty and external disturbance.TThe control signal for each actuator is quantized by sector-bounded quantizers,including the logarithmic quantizer and the hysteresis quantizer.By describing the impact of quantization in a new affine model and introducing a smooth function and a novel form of the control signal,the influence caused by input quantization and external disturbance is properly compensated for.Moreover,with the aid of the adaptive control technique,our approach can achieve attitude tracking without the explicit knowledge of inertial parameters.Unlike existing attitude control schemes for spacecraft,in this paper,the quantization parameters can be unknown,and the bounds of inertial parameters and disturbance are also not needed.In addition to proving the stability of the closed-loop system,the relationship between the control performance and design parameters is analyzed.Simulation results are presented to illustrate the effectiveness of the proposed scheme.展开更多
The finite-time synchronization of fractional-order multi-weighted complex networks(FMCNs)with uncertain parameters and external disturbances is studied.Firstly,based on fractional calculus characteristics and Lyapuno...The finite-time synchronization of fractional-order multi-weighted complex networks(FMCNs)with uncertain parameters and external disturbances is studied.Firstly,based on fractional calculus characteristics and Lyapunov stability theory,quantized controllers are designed to guarantee that FMCNs can achieve synchronization in a limited time with and without coupling delay,respectively.Then,appropriate parameter update laws are obtained to identify the uncertain parameters in FMCNs.Finally,numerical simulation examples are given to validate the correctness of the theoretical results.展开更多
This paper is concerned with the parameter estimation of deterministic autoregressive moving average(DARMA)systems with quantization data.The estimation algorithms adopted here are the least squares(LS)and the forgett...This paper is concerned with the parameter estimation of deterministic autoregressive moving average(DARMA)systems with quantization data.The estimation algorithms adopted here are the least squares(LS)and the forgetting factor LS,and the signal quantizer is of uniform,that is,with uniform quantization error.The authors analyse the properties of the LS and the forgetting factor LS,and establish the boundedness of the estimation errors and a relationship of the estimation errors with the size of quantization error,which implies that the smaller the quantization error is,the smaller the estimation error is.A numerical example is given to demonstrate theorems.展开更多
文摘With the development of multimedia presentation technology,image acquisition technology and the Internet industry,long-distance communication methods have changed from the previous letter,the audio to the current audio/video.And the proportion of video in work,study and entertainment keeps increasing,high-definition video is getting more and more attention.Due to the limits of the network environment and storage capacity,the original video must be encoded to be efficiently transmitted and stored.High Efficient Video Coding(HEVC)requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process.The optimal quantization parameter is calculated by comparing the rate distortion cost.In this paper,we propose a fast decision method for HEVC quantization parameters selection based on convolutional neural network,which saves video’s encoding time.
文摘The high-efficiency video coder(HEVC)is one of the most advanced techniques used in growing real-time multimedia applications today.However,they require large bandwidth for transmission through bandwidth,and bandwidth varies with different video sequences/formats.This paper proposes an adaptive information-based variable quantization matrix(AIVQM)developed for different video formats having variable energy levels.The quantization method is adapted based on video sequence using statistical analysis,improving bit budget,quality and complexity reduction.Further,to have precise control over bit rate and quality,a multi-constraint prune algorithm is proposed in the second stage of the AI-VQM technique for pre-calculating K numbers of paths.The same should be handy to selfadapt and choose one of the K-path automatically in dynamically changing bandwidth availability as per requirement after extensive testing of the proposed algorithm in the multi-constraint environment for multiple paths and evaluating the performance based on peak signal to noise ratio(PSNR),bit-budget and time complexity for different videos a noticeable improvement in rate-distortion(RD)performance is achieved.Using the proposed AIVQM technique,more feasible and efficient video sequences are achieved with less loss in PSNR than the variable quantization method(VQM)algorithm with approximately a rise of 10%–20%based on different video sequences/formats.
基金This work was supported by the National Natural Science Foundation of China(62076025).
文摘This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.
文摘In this paper,an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization,inertial uncertainty and external disturbance.TThe control signal for each actuator is quantized by sector-bounded quantizers,including the logarithmic quantizer and the hysteresis quantizer.By describing the impact of quantization in a new affine model and introducing a smooth function and a novel form of the control signal,the influence caused by input quantization and external disturbance is properly compensated for.Moreover,with the aid of the adaptive control technique,our approach can achieve attitude tracking without the explicit knowledge of inertial parameters.Unlike existing attitude control schemes for spacecraft,in this paper,the quantization parameters can be unknown,and the bounds of inertial parameters and disturbance are also not needed.In addition to proving the stability of the closed-loop system,the relationship between the control performance and design parameters is analyzed.Simulation results are presented to illustrate the effectiveness of the proposed scheme.
文摘The finite-time synchronization of fractional-order multi-weighted complex networks(FMCNs)with uncertain parameters and external disturbances is studied.Firstly,based on fractional calculus characteristics and Lyapunov stability theory,quantized controllers are designed to guarantee that FMCNs can achieve synchronization in a limited time with and without coupling delay,respectively.Then,appropriate parameter update laws are obtained to identify the uncertain parameters in FMCNs.Finally,numerical simulation examples are given to validate the correctness of the theoretical results.
基金supported by National Key R&D Program of China under Grant No.2018YFA0703800the National Natural Science Foundation of China under Grant No.61877057。
文摘This paper is concerned with the parameter estimation of deterministic autoregressive moving average(DARMA)systems with quantization data.The estimation algorithms adopted here are the least squares(LS)and the forgetting factor LS,and the signal quantizer is of uniform,that is,with uniform quantization error.The authors analyse the properties of the LS and the forgetting factor LS,and establish the boundedness of the estimation errors and a relationship of the estimation errors with the size of quantization error,which implies that the smaller the quantization error is,the smaller the estimation error is.A numerical example is given to demonstrate theorems.
