针对视频点云压缩(video-based point cloud compression,V-PCC)把点云序列分解成2D的非规则图像块(patch)进行编码,破坏点云连续性,不利于后续帧间预测的问题,设计一种三维配准帧间预测结合V-PCC帧间预测的改进算法.首先,为了提高点云...针对视频点云压缩(video-based point cloud compression,V-PCC)把点云序列分解成2D的非规则图像块(patch)进行编码,破坏点云连续性,不利于后续帧间预测的问题,设计一种三维配准帧间预测结合V-PCC帧间预测的改进算法.首先,为了提高点云配准的有效性,设计一种基于运动一致性的二叉树的粗分割和进一步八叉树细分割的算法,使得每一块点云在配准时具有一致性运动和准确的对应性.进一步地,为了保证三维帧间配准预测的可靠性,对分割后的块进行三维配准帧间预测并计算误差.对于误差小于一定阈值的块直接熵编码块索引和运动信息;对于误差大于阈值的块,则融合并使用V-PCC的帧间估计.实验结果表明,本方法进一步提高了V-PCC的编码性能.展开更多
基于视频的点云压缩(Video based point cloud compression, V-PCC)为压缩动态点云提供了高效的解决方案,但V-PCC从三维到二维的投影使得三维帧间运动的相关性被破坏,降低了帧间编码性能.针对这一问题,提出一种基于V-PCC改进的自适应分...基于视频的点云压缩(Video based point cloud compression, V-PCC)为压缩动态点云提供了高效的解决方案,但V-PCC从三维到二维的投影使得三维帧间运动的相关性被破坏,降低了帧间编码性能.针对这一问题,提出一种基于V-PCC改进的自适应分割的视频点云多模式帧间编码方法,并依此设计了一种新型动态点云帧间编码框架.首先,为实现更精准的块预测,提出区域自适应分割的块匹配方法以寻找最佳匹配块;其次,为进一步提高帧间编码性能,提出基于联合属性率失真优化(Rate distortion optimization, RDO)的多模式帧间编码方法,以更好地提高预测精度和降低码率消耗.实验结果表明,提出的改进算法相较于V-PCC实现了-22.57%的BD-BR (Bjontegaard delta bit rate)增益.该算法特别适用于视频监控和视频会议等帧间变化不大的动态点云场景.展开更多
首先介绍了储能变流器在光储微网系统不同运行模式下的控制策略。在并网运行模式下,针对光储微网系统中公共连接点(point of common coupling,PCC)处的电压会受到负载变化和光伏出力波动的影响,提出一种基于储能的电压管理控制策略。该...首先介绍了储能变流器在光储微网系统不同运行模式下的控制策略。在并网运行模式下,针对光储微网系统中公共连接点(point of common coupling,PCC)处的电压会受到负载变化和光伏出力波动的影响,提出一种基于储能的电压管理控制策略。该控制策略可通过储能变流器的PQ控制,来维持PCC点母线电压在额定电压?10%的范围内波动,从而满足负载对电压质量的需求。当配电网发生故障或储能出力已达功率限值仍不能维持PCC点母线电压在允许范围内时,光储微网切换为孤岛运行模式,此时储能系统采用V/f控制来保证微网系统电压和频率的稳定,并联合光伏系统共同为负载供电。建立光储微网系统的仿真模型,给出变流器的控制策略以及PCC点母线电压的控制流程,仿真结果验证了提出控制策略的有效性。展开更多
In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mod...In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mode of Coding Unit(CU)is searched by brute-force strategy,which greatly increases the complexity of the encoding process.To address this issue,we first propose a simple and effective Portable Perceptron Network(PPN)-based fast mode decision method for V-PCC under Random Access(RA)configuration.Second,we extract seven simple hand-extracted features for input into the PPN network.Third,we design an adaptive loss function,which can calculate the loss by allocating different weights according to different Rate-Distortion(RD)costs,to train our PPN network.Finally,experimental results show that the proposed method can save encoding complexity of 43.13%with almost no encoding efficiency loss under RA configuration,which is superior to the state-of-the-art methods.The source code is available at https://github.com/Mesks/PPNforV-PCC.展开更多
文摘针对视频点云压缩(video-based point cloud compression,V-PCC)把点云序列分解成2D的非规则图像块(patch)进行编码,破坏点云连续性,不利于后续帧间预测的问题,设计一种三维配准帧间预测结合V-PCC帧间预测的改进算法.首先,为了提高点云配准的有效性,设计一种基于运动一致性的二叉树的粗分割和进一步八叉树细分割的算法,使得每一块点云在配准时具有一致性运动和准确的对应性.进一步地,为了保证三维帧间配准预测的可靠性,对分割后的块进行三维配准帧间预测并计算误差.对于误差小于一定阈值的块直接熵编码块索引和运动信息;对于误差大于阈值的块,则融合并使用V-PCC的帧间估计.实验结果表明,本方法进一步提高了V-PCC的编码性能.
文摘基于视频的点云压缩(Video based point cloud compression, V-PCC)为压缩动态点云提供了高效的解决方案,但V-PCC从三维到二维的投影使得三维帧间运动的相关性被破坏,降低了帧间编码性能.针对这一问题,提出一种基于V-PCC改进的自适应分割的视频点云多模式帧间编码方法,并依此设计了一种新型动态点云帧间编码框架.首先,为实现更精准的块预测,提出区域自适应分割的块匹配方法以寻找最佳匹配块;其次,为进一步提高帧间编码性能,提出基于联合属性率失真优化(Rate distortion optimization, RDO)的多模式帧间编码方法,以更好地提高预测精度和降低码率消耗.实验结果表明,提出的改进算法相较于V-PCC实现了-22.57%的BD-BR (Bjontegaard delta bit rate)增益.该算法特别适用于视频监控和视频会议等帧间变化不大的动态点云场景.
文摘首先介绍了储能变流器在光储微网系统不同运行模式下的控制策略。在并网运行模式下,针对光储微网系统中公共连接点(point of common coupling,PCC)处的电压会受到负载变化和光伏出力波动的影响,提出一种基于储能的电压管理控制策略。该控制策略可通过储能变流器的PQ控制,来维持PCC点母线电压在额定电压?10%的范围内波动,从而满足负载对电压质量的需求。当配电网发生故障或储能出力已达功率限值仍不能维持PCC点母线电压在允许范围内时,光储微网切换为孤岛运行模式,此时储能系统采用V/f控制来保证微网系统电压和频率的稳定,并联合光伏系统共同为负载供电。建立光储微网系统的仿真模型,给出变流器的控制策略以及PCC点母线电压的控制流程,仿真结果验证了提出控制策略的有效性。
基金supported by the National Natural Science Foundation of China(No.62001209).
文摘In Video-based Point Cloud Compression(V-PCC),2D videos to be encoded are generated by 3D point cloud projection,and compressed by High Efficiency Video Coding(HEVC).In the process of 2D video compression,the best mode of Coding Unit(CU)is searched by brute-force strategy,which greatly increases the complexity of the encoding process.To address this issue,we first propose a simple and effective Portable Perceptron Network(PPN)-based fast mode decision method for V-PCC under Random Access(RA)configuration.Second,we extract seven simple hand-extracted features for input into the PPN network.Third,we design an adaptive loss function,which can calculate the loss by allocating different weights according to different Rate-Distortion(RD)costs,to train our PPN network.Finally,experimental results show that the proposed method can save encoding complexity of 43.13%with almost no encoding efficiency loss under RA configuration,which is superior to the state-of-the-art methods.The source code is available at https://github.com/Mesks/PPNforV-PCC.