基于蚁群算法和神经网络的船舶图像压缩方法  

Ship image compression method based on ant colony algorithm and neural network

作　　者：李晓岩[1] 苏娜[1] LI Xiao-yan;SU Na(Qingdao Huanghai University,Qingdao 266555,China)

机构地区：[1]青岛黄海学院,山东青岛266555

出　　处：《舰船科学技术》2022年第8期165-168,共4页Ship Science and Technology

摘　　要：本文基于蚁群算法和神经网络提出一种新的船舶图像压缩方法。分析图像的小波变化率,通过分割法在原始图像上构建搜索空间,采用全局搜索进行数据匹配,从而降低匹配的平均值,提高对数据进行分形和编码时的平均速度,采集船舶图像数据在不同方向的纹理信息,通过分形预测实现信息压缩。实验结果表明,基于蚁群算法和神经网络的船舶图像压缩方法与传统方法相比,消耗的压缩时间更短,压缩比更高,综合能力更强,更适合于实际工作中。This paper proposes a new ship image compression method based on the ant colony algorithm and the neural network. Analyze the wavelet change rate of the image, construct a search space on the original image by segmentation method, and use rene search to match the data, thereby reducing the average value of matching, improving the average speed of fractal and encoding data, and collecting ship image data. The texture information in different directions is compressed by fractal prediction. The experimental results show that compared with the traditional methods, the ant colony algorithm and neural network ship image compression method consumes shorter compression time, higher compression ratio and stronger comprehensive ability, and is more suitable for practical work.

关 键 词：蚁群算法 神经网络 船舶图像 图像压缩 压缩方法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]