Wireless Communications & Mobile Computing最新文献_第2页

Image Compression for Wireless Sensor Network: A Model Segmentation-Based Compressive Autoencoder 无线传感器网络图像压缩:一种基于模型分割的压缩自编码器

4区计算机科学 Q3 Engineering

Wireless Communications & Mobile Computing

Pub Date : 2023-10-25 DOI: 10.1155/2023/8466088

Xuecai Bao, Chen Ye, Longzhe Han, Xiaohua Xu

Aiming at the problems of image quality, compression performance, and transmission efficiency of image compression in wireless sensor networks (WSN), a model segmentation-based compressive autoencoder (MS-CAE) is proposed. In the proposed algorithm, we first divide each image in the dataset into pixel blocks and design a novel deep image compression network with a compressive autoencoder to form a compressed feature map by encoding pixel blocks. Then, the reconstructed image is obtained by using the quantized coefficients of the quantizer and splicing the decoded feature maps in order. Finally, the deep network model is segmented into two parts: the encoding network and the decoding network. The weight parameters of the encoding network are deployed to the edge device for the compressed image in the sensor network. For high-quality reconstructed images, the weight parameters of the decoding network are deployed to the cloud system. Experimental results demonstrate that the proposed MS-CAE obtains a high signal-to-noise ratio (PSNR) for the details of the image, and the compression ratio at the same bit per pixel (bpp) is significantly higher than that of the compared image compression algorithms. It also indicates that the MS-CAE not only greatly relieves the pressure of the hardware system in sensor network but also effectively improves image transmission efficiency and solves the deployment problem of image monitoring in remote and energy-poor areas.

针对无线传感器网络(WSN)中图像压缩存在的图像质量、压缩性能和传输效率问题，提出了一种基于模型分割的压缩自编码器(MS-CAE)。在该算法中，我们首先将数据集中的每张图像划分为像素块，并设计了一种新的带有压缩自编码器的深度图像压缩网络，通过对像素块进行编码形成压缩特征映射。然后，利用量化器的量化系数，将解码后的特征映射按顺序拼接，得到重构图像。最后，将深度网络模型划分为编码网络和解码网络两部分。将编码网络的权值参数部署到传感器网络中压缩图像的边缘设备上。对于高质量的重构图像，将解码网络的权重参数部署到云系统中。实验结果表明，所提出的MS-CAE算法对图像细节具有较高的信噪比(PSNR)，在相同比特每像素(bpp)下的压缩比明显高于所比较的图像压缩算法。研究表明，MS-CAE不仅极大地缓解了传感器网络硬件系统的压力，而且有效地提高了图像传输效率，解决了偏远和能源贫乏地区图像监控的部署问题。

{"title":"Image Compression for Wireless Sensor Network: A Model Segmentation-Based Compressive Autoencoder","authors":"Xuecai Bao, Chen Ye, Longzhe Han, Xiaohua Xu","doi":"10.1155/2023/8466088","DOIUrl":"https://doi.org/10.1155/2023/8466088","url":null,"abstract":"Aiming at the problems of image quality, compression performance, and transmission efficiency of image compression in wireless sensor networks (WSN), a model segmentation-based compressive autoencoder (MS-CAE) is proposed. In the proposed algorithm, we first divide each image in the dataset into pixel blocks and design a novel deep image compression network with a compressive autoencoder to form a compressed feature map by encoding pixel blocks. Then, the reconstructed image is obtained by using the quantized coefficients of the quantizer and splicing the decoded feature maps in order. Finally, the deep network model is segmented into two parts: the encoding network and the decoding network. The weight parameters of the encoding network are deployed to the edge device for the compressed image in the sensor network. For high-quality reconstructed images, the weight parameters of the decoding network are deployed to the cloud system. Experimental results demonstrate that the proposed MS-CAE obtains a high signal-to-noise ratio (PSNR) for the details of the image, and the compression ratio at the same bit per pixel (bpp) is significantly higher than that of the compared image compression algorithms. It also indicates that the MS-CAE not only greatly relieves the pressure of the hardware system in sensor network but also effectively improves image transmission efficiency and solves the deployment problem of image monitoring in remote and energy-poor areas.","PeriodicalId":49359,"journal":{"name":"Wireless Communications & Mobile Computing","volume":"14 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134974085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}