CNN-Based Fast HEVC Quantization Parameter Mode Decision

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.

High-Efficiency Video Coder in Pruned Environment Using Adaptive Quantization Parameter Selection

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.

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

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.

Robust Adaptive Attitude Control for Non-rigid Spacecraft With Quantized Control Input

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.

Finite-time synchronization of uncertain fractional-order multi-weighted complex networks with external disturbances via adaptive quantized control

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.

自动语音识别模型压缩算法综述

随着深度学习技术的发展,自动语音识别任务模型的参数数量越来越庞大,使得模型的计算开销、存储需求和功耗花费逐渐增加,难以在资源受限设备上部署.因此对基于深度学习的自动语音识别模型进行压缩,在降低模型大小的同时尽量保持原有性能具有重要价值.针对上述问题,全面综述了近年来该领域的主要工作,将其归纳为知识蒸馏、模型量化、低秩分解、网络剪枝、参数共享以及组合模型几类方法,并进行了系统综述,为模型在资源受限设备的部署提供可选的解决方案.

基于时域依赖的编码树单元级零延时码率控制算法

基于高效视频编码标准的x265编码器根据图像复杂度来分配比特,复杂图像往往包含运动变化较大的高频信息,其时域相关性较弱且消耗较多比特,导致分配给运动变化平缓图像的比特减少,进而影响编码质量且码率波动较大。同时,x265编码器采用独立率失真优化技术编码,忽略了编码单元间在时域上的相关性,进而损失编码性能。针对上述问题,提出一种基于时域依赖的编码树单元级码率控制算法。首先,根据迭代策略寻找最合适的量化参数进行帧级比特分配;其次,建立零延时的失真时域反向传播模型并计算失真影响因子;最后,将失真影响因子用于调整编码单元的拉格朗日乘子及量化参数。实验结果显示,相较于x265-3.6的码率控制算法,所提算法的平均BD-rate码率节省达到5.6%。

基于帧间量化参数强度值检测深度伪造人像视频

深度学习算法的发展为伪造人像视频的创作增添了“助力”,应运而生的深度伪造检测成为学界和业界关注的焦点。基于深度学习的伪造视频检测技术需要耗费大量时间与算力训练分类器,深度学习网络的黑盒性和不可解释性也困扰着深度伪造人像视频取证研究。围绕深度伪造人像视频的真实性取证问题,本文提出一种基于视频帧间量化参数强度值的检测方法,并通过二元Logistic回归方程给出参考阈值。实验结果表明:本文的方法在检测利用DeepFaceLab换脸平台合成的深度伪造人像视频中表现出良好的准确率和鲁棒性。所提方法有助于对可疑人像视频真实性进行判断,为涉案人像视频提供取证依据。

异构环境感知的分布式神经网络训练模型

针对分布式神经网络训练在异构环境中训练速度慢、资源利用率低的问题,提出一种异构环境感知的分布式神经网络训练模型(H-PS)。根据计算节点当前状态动态调度训练任务,使计算节点能够在相同时间完成训练任务,提高资源利用率。提出通信与计算并行策略,参数服务器与计算节点传输模型参数期间,计算节点持续模型计算,进一步提高资源利用率。使用灵活的量化策略,压缩神经网络模型参数,减少参数服务器与计算节点的通信开销。使用新兴的容器集群进行实验,结果表明,与现有方法相比,H-PS训练时间缩短1.4~3.5倍。

基于参考单元编码失真时域影响的率失真优化算法

针对通用视频编码标准H.266/VVC的独立率失真优化技术未考虑参考单元编码失真的时域传播影响而损失编码性能的问题,提出一种基于编码失真时域传播影响的低延时率失真优化算法。首先,根据视频图像的时域连续性特征,由运动补偿预测误差及重建误差来计算编码失真的时域传播影响权重;其次,建立基于编码失真时域传播影响的率失真优化模型;最后,将编码失真的时域传播影响权重用于调整编码单元的拉格朗日乘子及量化参数。实验结果显示,在低延时P帧和B帧配置下,相较于基准算法,BD-rate码率节省分别达到1.6%和0.9%,编码时间分别下降2.93%和6.02%。实验结果表明,该算法在不增加编码复杂度的条件下有效提升了编码性能,适用于实时编码应用场景。

LS-Based Parameter Estimation of DARMA Systems with Uniformly Quantized Observations

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.

模拟降雨条件下坡面细沟形态演变与量化方法

为探明坡面土壤侵蚀过程中细沟的演变规律,研究细沟形态特征量化方法是非常重要的。利用室内土槽模拟降雨试验,对比了45 mm/h、87 mm/h和127 mm/h雨强下容重分别为1.30 g/cm3和1.05 g/cm3两种黄土在20°陡坡坡面细沟发育形态的差异,并分别采用分形维数、拓扑参数对细沟沟网的形态特征进行量化描述,引入地貌信息熵理论进行坡面地貌信息熵计算,然后对各量化参数的敏感性进行了分析。结果表明,分形维数作为整体性的量化参数,对描述发育成熟的细沟形态差异不够灵敏;坡面地貌信息熵能够反映侵蚀过程中侵蚀程度的变化,对细沟发育程度的描述较为灵敏;细沟沟网的拓扑特征参数较好地体现了沟网内部结构的差异,可用于描述沟网内部结构特征的较灵敏参数。

利用调频率匹配的DRFM欺骗干扰检测方法

针对转发式线性调频(LFM)有源欺骗干扰的检测问题,提出了基于干扰信号谐波分量调频率匹配的检测方法.数字射频存储器存在谐波效应特性,该方法通过分析干扰信号的频谱特征规律,建立谐波分量调频率参数库.在雷达距离门内同时存在目标回波和干扰信号的情形下,利用分数阶傅里叶变换检测雷达回波中LFM信号分量并估计其调频率,通过与参数库进行匹配分析,实现干扰信号的检测.仿真结果验证了该方法的有效性.

基于寿命量化的混合储能系统协调控制参数优化

混合储能系统控制策略中参数对控制效果具有一定影响,应量化分析其影响并进行参数优化。文中采用考虑自放电率和充放电效率等因素影响的储能系统数学模型;在基于锂电池充放电状态的混合储能系统控制策略基础上,提出超级电容储能荷电状态协调响应裕量指标;结合适用于实际不规则充放电应用的锂电池寿命量化模型,建立基于锂电池量化寿命的混合储能系统协调控制参数优化模型。算例分析表明,合理选择混合储能系统协调控制参数可延长锂电池使用寿命,进而提高混合储能系统的整体性能。

量子化学在缓蚀剂研究中的应用

近年来,量子化学在缓蚀剂分子结构和缓蚀机理研究中得到了广泛应用。概述了半经验算法,从头算和密度泛函方法3种量子化学理论计算方法及其在缓蚀剂研究中的应用,并对其在该领域的发展前景进行了展望。

H.264帧内预测快速算法

在研究H．264帧内预测算法的基础上，提出一种新颖实用的自适应帧内预测快速算法．首先，利用前一帧和当前帧对应位置宏块的帧内预测模式进行预测，从而减少当前宏块的预测模式数量，降低编码复杂度；然后根据量化因子。自适应地插入使用各种帧内预测模式的修正帧，并改变候选预测块的数量来保证帧内预测快速算法的准确性．实验结果表明，在码率增加小于3％，恢复图像平均峰值信噪比下降不到0．04dB的情况下，使用帧内预测快速算法，编码速度可提高22％～35％，满足实时视频通信的要求．该算法与H．264标准兼容，可用于实际视频通信产品．

原发性骨质疏松症血瘀与疼痛的关系研究

目的分析原发性骨质疏松症患者血瘀与骨质疏松性疼痛的关系。方法收集2014年6月至2015年10月广州中医药大学第一附属医院确诊为原发性骨质疏松症血瘀证患者99例,检测患者腰椎骨密度及血细胞参数(MCV、RDW-CV、MPV、PDW)水平,并对所有患者进行血瘀评分及疼痛评分。结果患者疼痛评分、血瘀评分随着年龄增高而呈不断升高的趋势,且疼痛评分与血瘀评分之间存在中度正相关(r=0.535,P<0.01);与RDW-CV呈低度正相关(r=0.357,P<0.01)。结论原发性骨质疏松症患者骨质疏松性疼痛与血瘀存在一定的相关性,血瘀越严重,疼痛程度也越严重,提示血瘀在骨质疏松性疼痛致病机制中起着重要作用。

高效率视频编码快速模式判决算法

为了降低高效率视频编码(HEVC)的编码复杂度,提出一种新的快速模式判决算法。考虑到视频帧的纹理特性和编码中所采用的量化参数影响最优编码单元(CU)模式的选择,首先提取前一帧的平均分割层数和其最大编码单元(LCU)的最小分割层数来预测当前帧对应位置处LCU的最小分割层数,以跳过部分大块CU的模式判决;然后计算当前CU的运动矢量差值和预测残差系数来决定CU的最大分割层数,以避免小块CU的模式判决。实验结果表明,与原始的HEVC编码方法相比,高效率视频编码快速模式判决算法可平均降低51%的编码复杂度,而编码比特率平均只上升了0.69%。

差分和加权Mel倒谱混合参数应用于说话人识别

说话人识别是信息技术和生物学的新一代身份验证方式,在说话人识别的研究中,特征参数的提取直接影响到识别系统最终的识别效率.通过对Mel频率倒谱系数特征参数进行分析研究,基于Mel频率倒谱系数改进加权函数,将体现个人语音特性的加权特征参数与反映语音帧间变化的差分Mel频率倒谱系数进行维度筛选,再进行参数混合.实验结果表明,通过改进加权函数提取得到的特征参数与差分Mel频率倒谱系数的混合参数在矢量量化的说话人识别系统中,码本容量为16和32时可以达到100%的识别率.

SELP声码器基音周期参数量化合成改进算法

针对正弦激励线性预测(SELP)声码器在解码端使用整型基音周期会引入额外舍入误差的问题,提出了一种码本整型化修正算法,该算法通过对码本中的各个码字在线性域进行取整,达到与解码端反量化过程一致的效果。测试结果表明,该算法能够有效地降低基音周期对数域矢量量化的量化误差,并且能够将合成语音的平均意见得分(MOS)提高0.05左右,明显改善合成语音质量。