基于视觉注意模型的红外图像分级压缩方法

Hierarchical infrared image compression method based on visual attention model

针对红外成像末制导武器人在回路模式中回传图像高压缩比与高保真度之间的矛盾,提出了一种基于视觉注意模型的红外图像分级压缩方法。在采用视觉注意模型提取得到红外图像显著区域的基础上,按照显著区域、背景区域、过渡区域对图像进行三级划分,然后根据不同区域图像中信息的重要程度采取不同的压缩方式,进而实现对整幅图像的压缩。实验结果表明:该方法在保留目标重要信息的同时,大大减少了图像传输的数据量,与传统的整图压缩方法相比,更适用于人在回路的应用场合。

Aiming at the contradiction between high compression ratio and high fidelity of the return image in man-in-the-loop （MITL） of infrared imaging guidance weapons, a hierarchical infrared image compression algorithm based on visual attention model was proposed. On the basis of salient regions of infrared image extracted by visual attention model, salient regions, background regions and transition regions were divided and compressed by different compression methods according to the importance of information of different image regions, in this way the compression of the whole image was realized. The experimental result shows that the proposed algorithm can not only preserve the important information of typical targets, but also decrease the data size in image transmission, and it is more suitable for MITL operation mode than the traditional full image compression method.