基于信息熵的目标图像信息压缩提取算法

Target image information compression and extraction algorithm based on information entropy

摘要:

当前图像信息提取面临海量数据传输与信道通信能力的约束。为此，构建了多层图像信息提取系统，克服传输时间和通信容量的限制。基于信息熵理论，以图像为主的传感信息作为输入的最小信息熵，创立多层目标图像信息提取算法；结合图像特征工程，提高图像的特征提取和推断效果；利用传感器图像数据，提取图像最小体量关键信息。实验验证该算法在不丢失完整有效的信息下，对图像信息压缩比提高至10⁶，解决了以低传输量和低传输带宽完成实时可靠的远距离图像信息传输问题。

Abstract:

The current extraction of image information is constrained by the transmission of massive data and the limitations of channel communication capabilities. To address this, a multi-layer image information extraction system has been constructed to overcome the limitations of transmission time and communication capacity. Based on information entropy theory, a multi-layer target image information extraction algorithm is established, using the minimum information entropy of image-based sensor information as input. By combining image feature engineering, the algorithm enhances the feature extraction and inference of images. It also utilizes sensor image data to extract the minimum volume of key information from images. Experiments have verified that this algorithm can increase the image information compression ratio to 10⁶ without losing complete and effective information. This effectively solves the problem of real-time and reliable long-distance image information transmission with low data volume and low bandwidth.