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学者姓名：郑明魁
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Abstract ：
The comprehension of 3D semantic scenes holds paramount significance in autonomous driving and robotics technology. Nevertheless, the simultaneous achievement of real-time processing and high precision in complex, expansive outdoor environments poses a formidable challenge. In response to this challenge, we propose a novel occupancy network named RTONet, which is built on a teacher-student model. To enhance the ability of the network to recognize various objects, the decoder incorporates dilated convolution layers with different receptive fields and utilizes a multi-path structure. Furthermore, we develop an automatic frame selection algorithm to augment the guidance capability of the teacher network. The proposed method outperforms the existing grid-based approaches in semantic completion (mIoU), and achieves the state-of-the-art performance in terms of real-time inference speed while exhibiting competitive performance in scene completion (IoU) on the SemanticKITTI benchmark. IEEE
Keyword ：
Decoding Decoding Deep Learning for Visual Perception Deep Learning for Visual Perception Feature extraction Feature extraction Laser radar Laser radar LiDAR LiDAR Mapping Mapping Occupancy Grid Occupancy Grid Point cloud compression Point cloud compression Real-time systems Real-time systems Semantics Semantics Semantic Scene Understanding Semantic Scene Understanding Three-dimensional displays Three-dimensional displays
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| GB/T 7714 | Lai, Q. , Zheng, H. , Feng, X. et al. RTONet: Real-Time Occupancy Network for Semantic Scene Completion [J]. | IEEE Robotics and Automation Letters , 2024 , 9 (10) : 1-8 . |
| MLA | Lai, Q. et al. "RTONet: Real-Time Occupancy Network for Semantic Scene Completion" . | IEEE Robotics and Automation Letters 9 . 10 (2024) : 1-8 . |
| APA | Lai, Q. , Zheng, H. , Feng, X. , Zheng, M. , Chen, H. , Chen, W. . RTONet: Real-Time Occupancy Network for Semantic Scene Completion . | IEEE Robotics and Automation Letters , 2024 , 9 (10) , 1-8 . |
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The monitoring and analysis of massive data from radio spectrum monitoring are essential components of radio regulation work. To address this, the paper proposes a lossless compression method based on wavelet-like transform for radio spectrum monitoring data. This method first converts the one-dimensional spectrum signal into a two-dimensional matrix based on temporal correlation. Once transformed into a two-dimensional matrix, there is redundancy in both the horizontal and vertical directions. The algorithm employs a convolutional neural network to replace the prediction and update modules in traditional wavelet transform, and introduces an adaptive compression block to handle features of different dimensions, thereby obtaining a more compact representation of spectrum data. Furthermore, the paper designs a context-based deep entropy model, which utilizes the wavelet-like transform′ s different subband coefficients to obtain entropy coding parameters, estimating cumulative probabilities to achieve spectrum data compression. Experimental results indicate that the proposed algorithm achieves additional performance improvements compared to existing traditional lossless compression methods for spectrum data, such as Deflate. Moreover, when compared with typical two-dimensional image lossless compression methods like JPEG2000, PNG, and JPEG-LS, the proposed method achieves over 20% better compression effectiveness. © 2024 Editorial Office of EMI Journal. All rights reserved.
Keyword ：
convolutional neural network convolutional neural network entropy coding entropy coding lossless compression lossless compression spectrum monitoring data spectrum monitoring data wavelet-like transform wavelet-like transform
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| GB/T 7714 | Zhang, C. , Zheng, M. , Liu, H. et al. Lossless compression method for radio spectrum data based on wavelet-like transform; [一种基于类小波变换的无线电频谱监测数据无损压缩方法] [J]. | Journal of Electronic Measurement and Instrumentation , 2024 , 38 (7) : 152-158 . |
| MLA | Zhang, C. et al. "Lossless compression method for radio spectrum data based on wavelet-like transform; [一种基于类小波变换的无线电频谱监测数据无损压缩方法]" . | Journal of Electronic Measurement and Instrumentation 38 . 7 (2024) : 152-158 . |
| APA | Zhang, C. , Zheng, M. , Liu, H. , Yi, T. , Li, S. , Chen, Z. . Lossless compression method for radio spectrum data based on wavelet-like transform; [一种基于类小波变换的无线电频谱监测数据无损压缩方法] . | Journal of Electronic Measurement and Instrumentation , 2024 , 38 (7) , 152-158 . |
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For moving cameras, the video content changes significantly, which leads to inaccurate prediction in traditional inter prediction and results in limited compression efficiency. To solve these problems, first, we propose a camera pose-based background modeling (CP-BM) framework that uses the camera motion and the textures of reconstructed frames to model the background of the current frame. Compared with the reconstructed frames, the predicted background frame generated by CP-BM is more geometrically similar to the current frame in position and is more strongly correlated with it at the pixel level; thus, it can serve as a higher-quality reference for inter prediction, and the compression efficiency can be improved. Second, to compensate the motion of the background pixels, we construct a pixel-level motion vector field that can accurately describe various complex motions with only a small overhead. Our method is more general than other motion models because it has more degrees of freedom, and when the degrees of freedom are decreased, it encompasses other motion models as special cases. Third, we propose an optical flow-based depth estimation (OF-DE) method to synchronize the depth information at the codec, which is used to build the motion vector field. Finally, we integrate the overall scheme into the High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) reference software HM-16.7 and VTM-10.0. Experimental results demonstrate that in HM-16.7, for in-vehicle video sequences, our solution has an average Bj & oslash;ntegaard delta bit rate (BD-rate) gain of 8.02% and reduces the encoding time by 20.9% due to the superiority of our scheme in motion estimation. Moreover, in VTM-10.0 with affine motion compensation (MC) turned off and turned on, our method has average BD-rate gains of 5.68% and 0.56%, respectively.
Keyword ：
background modeling background modeling Bit rate Bit rate camera pose camera pose Cameras Cameras Computational modeling Computational modeling Encoding Encoding Estimation Estimation moving cameras moving cameras Predictive models Predictive models Video coding Video coding
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| GB/T 7714 | Fang, Zheng , Zheng, Mingkui , Chen, Pingping et al. Camera Pose-Based Background Modeling for Video Coding in Moving Cameras [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (5) : 4054-4069 . |
| MLA | Fang, Zheng et al. "Camera Pose-Based Background Modeling for Video Coding in Moving Cameras" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 5 (2024) : 4054-4069 . |
| APA | Fang, Zheng , Zheng, Mingkui , Chen, Pingping , Chen, Zhifeng , Oliver Wu, Dapeng . Camera Pose-Based Background Modeling for Video Coding in Moving Cameras . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (5) , 4054-4069 . |
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Abstract ：
无线电频谱监测海量数据存储和分析是无线电监管工作的重要组成部分.频谱数据具有时间相关性以及不同频点间的相关冗余,对此本文设计了一种基于类小波变换的无线电频谱监测数据无损压缩方法.该方法首先基于时间相关性将一维频谱信号转换成二维矩阵;转换成二维矩阵后数据在水平方向以及垂直方向都存在冗余,算法采用卷积神经网络来代替传统小波中的预测和更新模块,并引入了自适应压缩块来处理不同维度的特征,从而获得更紧凑的频谱数据表示.研究进一步设计了一种基于上下文的深度熵模型,利用类小波变换不同子带系数获得熵编码参数,以此估计累积概率,从而实现频谱数据的压缩.实验结果表明,与已有的Deflate等传统频谱监测数据无损压缩方法相比,本文算法有进一步的性能提升,与典型的JPEG2000、PNG、JPEG-LS等二维图像无损压缩方法相比,本文所提出的方法的压缩效果也提高了20％以上.
Keyword ：
卷积神经网络 卷积神经网络 无损压缩 无损压缩 熵编码 熵编码 类小波变换 类小波变换 频谱监测数据 频谱监测数据
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| GB/T 7714 | 张承琰 , 郑明魁 , 刘会明 et al. 一种基于类小波变换的无线电频谱监测数据无损压缩方法 [J]. | 电子测量与仪器学报 , 2024 , 38 (7) : 152-158 . |
| MLA | 张承琰 et al. "一种基于类小波变换的无线电频谱监测数据无损压缩方法" . | 电子测量与仪器学报 38 . 7 (2024) : 152-158 . |
| APA | 张承琰 , 郑明魁 , 刘会明 , 易天儒 , 李少良 , 陈祖儿 . 一种基于类小波变换的无线电频谱监测数据无损压缩方法 . | 电子测量与仪器学报 , 2024 , 38 (7) , 152-158 . |
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Abstract ：
The comprehension of 3D semantic scenes holds paramount significance in autonomous driving and robotics technology. Nevertheless, the simultaneous achievement of real-time processing and high precision in complex, expansive outdoor environments poses a formidable challenge. In response to this challenge, we propose a novel occupancy network named RTONet, which is built on a teacher-student model. To enhance the ability of the network to recognize various objects, the decoder incorporates dilated convolution layers with different receptive fields and utilizes a multi-path structure. Furthermore, we develop an automatic frame selection algorithm to augment the guidance capability of the teacher network. The proposed method outperforms the existing grid-based approaches in semantic completion (mIoU), and achieves the state-of-the-art performance in terms of real-time inference speed while exhibiting competitive performance in scene completion (IoU) on the SemanticKITTI benchmark.
Keyword ：
Decoding Decoding deep learning for visual perception deep learning for visual perception Feature extraction Feature extraction Laser radar Laser radar LiDAR LiDAR mapping mapping occupancy grid occupancy grid Point cloud compression Point cloud compression Real-time systems Real-time systems Semantics Semantics Semantic scene understanding Semantic scene understanding Three-dimensional displays Three-dimensional displays
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| GB/T 7714 | Lai, Quan , Zheng, Haifeng , Feng, Xinxin et al. RTONet: Real-Time Occupancy Network for Semantic Scene Completion [J]. | IEEE ROBOTICS AND AUTOMATION LETTERS , 2024 , 9 (10) : 8370-8377 . |
| MLA | Lai, Quan et al. "RTONet: Real-Time Occupancy Network for Semantic Scene Completion" . | IEEE ROBOTICS AND AUTOMATION LETTERS 9 . 10 (2024) : 8370-8377 . |
| APA | Lai, Quan , Zheng, Haifeng , Feng, Xinxin , Zheng, Mingkui , Chen, Huacong , Chen, Wenqiang . RTONet: Real-Time Occupancy Network for Semantic Scene Completion . | IEEE ROBOTICS AND AUTOMATION LETTERS , 2024 , 9 (10) , 8370-8377 . |
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本文提出一种基于全局注意力融合机制的可泛化NeRF框架，使用重投影特征提取作为前馈先验模块，设计了一种基于两阶段注意力聚合机制的解码器，实现了一种端到端可跨场景泛化的新视图合成方法。通过定性与定量的实验结果比较分析，证明本方案能够充分利用已知源图像中的深层特征信息，并且学习场景中多视图空间特征的联系，从而指导神经辐射场更好地重建未见场景的三维表征，更加精确地驱动体渲染在真实复杂环境中的光线上进行新视图渲染。
Keyword ：
注意力机制 注意力机制 神经辐射场 神经辐射场 视图合成 视图合成
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| GB/T 7714 | 刘阳 , 郑明魁 , 叶张帆 . 一种基于神经辐射场的跨场景新视图合成方法 [J]. | 广播电视网络 , 2024 , 31 (08) : 108-112 . |
| MLA | 刘阳 et al. "一种基于神经辐射场的跨场景新视图合成方法" . | 广播电视网络 31 . 08 (2024) : 108-112 . |
| APA | 刘阳 , 郑明魁 , 叶张帆 . 一种基于神经辐射场的跨场景新视图合成方法 . | 广播电视网络 , 2024 , 31 (08) , 108-112 . |
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视频帧插值技术应用广泛，其目的是在给定两个连续的视频帧条件下，生成中间帧。针对向投影过程中经常出现的多个像素投影到同一个位置的像素重叠问题，提出了一种基于深度值前向投影的视频帧插值模型。根据提出的深度估计模块的深度值对前向投影过程进行线性加权，并具有深度平移不变性，对重叠像素区域的前景物体边界和背景像素的像素重建有一定的效果提升。实验结果表明，所提出的算法在公开的视频帧内插数据集Vimeo-90k上测试结果良好，与其他算法相比，在PSNR、SSIM和LPIPS性能评价指标上均能达到较为优秀的性能指标，验证了算法的优越性。
Keyword ：
前向投影 前向投影 图像合成 图像合成 深度估计 深度估计 视频帧插值 视频帧插值 视频帧预测 视频帧预测
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| GB/T 7714 | 陈祖儿 , 郑明魁 , 张承琰 et al. 基于深度值前向投影的视频帧插值模型 [J]. | 信息技术与信息化 , 2024 , 4 (04) : 5-8 . |
| MLA | 陈祖儿 et al. "基于深度值前向投影的视频帧插值模型" . | 信息技术与信息化 4 . 04 (2024) : 5-8 . |
| APA | 陈祖儿 , 郑明魁 , 张承琰 , 易天儒 . 基于深度值前向投影的视频帧插值模型 . | 信息技术与信息化 , 2024 , 4 (04) , 5-8 . |
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压缩图像伪影去除作为一个图像恢复的子任务，目的是从有损压缩图像中恢复出高质量图像。现有基于Transformer的方法在计算自注意力时杂度过高，不适用于高分辨图像。有多种方法都被提出用来减小Transformer的计算量，但都会造成恢复效果下降。对此，提出一个CNN和Transformer结合的高效多层次特征聚合网络EMFANet。图像中在整体、部分和细小范围内存在全局、区域和局部特征，对于全局特征建模采用具有线性复杂度的自注意力，对于区域特征建模采用具有随机移位的窗口自注意力，对于局部特征建模采用基于通道注意力的轻量级卷积。经过实验验证，所提出的方法在JPEG压缩伪影去除任务上实现了较为先进的性能。
Keyword ：
压缩伪影去除 压缩伪影去除 多层次特征 多层次特征 注意力机制 注意力机制 深度学习 深度学习 移位窗口 移位窗口
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| GB/T 7714 | 易天儒 , 郑明魁 , 张承琰 et al. 基于多层次特征聚合的图像压缩伪影去除方法 [J]. | 信息技术与信息化 , 2024 , 5 (04) : 45-49 . |
| MLA | 易天儒 et al. "基于多层次特征聚合的图像压缩伪影去除方法" . | 信息技术与信息化 5 . 04 (2024) : 45-49 . |
| APA | 易天儒 , 郑明魁 , 张承琰 , 陈祖儿 . 基于多层次特征聚合的图像压缩伪影去除方法 . | 信息技术与信息化 , 2024 , 5 (04) , 45-49 . |
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激光雷达动态获取点云压缩在智能驾驶领域具有重要应用。为了应对点云序列时域冗余问题，本文设计了基于位姿状态的激光雷达点云帧间编码方法。在动态获取点云场景中，点云在三维空间上分布广泛且稀疏。对此，将其映射到二维距离图上。并在此基础上，结合激光雷达位姿关系，提出了一种高效的帧间预测编码方法，以消除时域冗余。由于三维物体遮挡的原因，激光雷达运动时易导致预测距离图产生空洞现象，影响预测编码性能。采用空洞填补方法可提高算法预测精度，并对预测残差进行量化和压缩。实验结果表明，相较于G-PCC等编码方法，所提出的方法在编码性能方面表现更为优越。
Keyword ：
激光雷达动态获取点云 激光雷达动态获取点云 点云压缩 点云压缩 空洞填补 空洞填补 距离图 距离图
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| GB/T 7714 | 李少良 , 郑明魁 , 石元龙 et al. 基于位姿状态的激光雷达点云帧间编码方法 [J]. | 信息技术与信息化 , 2024 , 4 (04) : 126-129 . |
| MLA | 李少良 et al. "基于位姿状态的激光雷达点云帧间编码方法" . | 信息技术与信息化 4 . 04 (2024) : 126-129 . |
| APA | 李少良 , 郑明魁 , 石元龙 , 张承琰 . 基于位姿状态的激光雷达点云帧间编码方法 . | 信息技术与信息化 , 2024 , 4 (04) , 126-129 . |
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电磁频谱数据是刻画电磁频谱态势的量化数字集合，频谱数据具有空间相关性、时间相关性以及频率相关性，在局部范围内的频谱数据和相邻频率上的数据存在冗余。针对上述问题，提出了一种基于位深度变换的无线电频谱数据压缩方法，首先将未经处理的无线电频谱监测数据经过数据转换模块转换成可便于后续压缩的频谱子图，然后采用传统编码方法压缩最高有效字节（HSB）频谱子图，最后设计了一种自回归熵模型来实现最低有效字节（LSB）频谱子图的高性能压缩并引入波前并行操作来加快解码速率。实验结果表明，其压缩率为39.05%，与已有的HUffman等传统无损压缩方法和无损图像压缩方法相比，所提出的方法在保证数据准确性的同时有着更好的压缩性能。
Keyword ：
图像编码 图像编码 无损压缩 无损压缩 神经网络 神经网络 频谱数据压缩 频谱数据压缩
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| GB/T 7714 | 张承琰 , 郑明魁 , 吴孔贤 et al. 一种基于位深度变换的无线电频谱数据压缩方法 [J]. | 信息技术与信息化 , 2024 , 4 (04) : 142-145 . |
| MLA | 张承琰 et al. "一种基于位深度变换的无线电频谱数据压缩方法" . | 信息技术与信息化 4 . 04 (2024) : 142-145 . |
| APA | 张承琰 , 郑明魁 , 吴孔贤 , 刘会明 . 一种基于位深度变换的无线电频谱数据压缩方法 . | 信息技术与信息化 , 2024 , 4 (04) , 142-145 . |
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