| 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[J].电子测量与仪器学报,2024,38(7):152-158 |
| 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 |
| Lossless compression method for radio spectrum data based on wavelet-like transform |
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| DOI： |
| 中文关键词: 频谱监测数据 无损压缩 类小波变换 卷积神经网络 熵编码 |
| 英文关键词:spectrum monitoring data lossless compression wavelet-like transform convolutional neural network entropy coding |
| 基金项目:国家自然科学基金（61902071）、福建省重大专项专题项目（2022HZ026007）、福州市科技重大项目（2022-ZD-002）、 福建省无线电监测站技术开发项目（01102220）资助 |
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| Author | Institution |
| Zhang Chengyan | College of physics and information engineering, Fuzhou University, Fuzhou 350108, China |
| Zheng Mingkui | College of physics and information engineering, Fuzhou University, Fuzhou 350108, China |
| Liu Huiming | Fujian Radio Monitoring Station, Fuzhou 350003, China |
| Yi Tianru | College of physics and information engineering, Fuzhou University, Fuzhou 350108, China |
| Li Shaoliang | College of physics and information engineering, Fuzhou University, Fuzhou 350108, China |
| Chen Zuer | College of physics and information engineering, Fuzhou University, Fuzhou 350108, China |
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| 全文下载次数: 246 |
| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
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