水下图像压缩是水声图像传输系统中必不可少并且至关重要的一个环节，有效的预测感知压缩图像的质量能使系统在压缩过程更好的调整压缩率，提高图像传输通信系统的效率。本文首先分别对压缩感知和嵌入式编码两种压缩策略下的水下压缩图像进行质量感知，然后利用图像活动性IAM（Image Activity Measurement）与BPP-SSIM（Bits Per Pixel and Structural SIMilarity）曲线间的映射进行建模并获得模型参数，从而根据图像的空域活动性、压缩率和压缩策略预测图像的压缩质量。实验结果表明本文所建立的模型能有效拟合水下图像的压缩质量曲线，根据模型中参数所具有的规律性能在小误差范围内预测出水下压缩图像的感知质量。本文所提出的方法能够有效的预测感知水下图像的压缩质量，并有效权衡压缩率与压缩质量之间的关系，减小发送端的数据缓存压力，提高水下图像通信系统的效率。
Abstract：
Underwater image compression is an important and essential part of an underwater image transmission system. An assessment and prediction method of effectively compressed image quality can assist the system in adjusting its compression ratio during the image compression process, thereby improving the efficiency of the image transmission system. This study first estimates the perceived quality of underwater image compression based on embedded coding compression and compressive sensing, then builds a model based on the mapping between image activity measurement (IAM) and bits per pixel and structural similarity (BPP-SSIM) curves, next obtains model parameters by linear fitting, and finally predicts the perceived quality of the image compression method based on IAM, compression ratio, and compression strategy. Experimental results show that the model can effectively fit the quality curve of underwater image compression. According to the rules of parameters in this model, the perceived quality of underwater compressed images can be estimated within a small error range. The presented method can effectively estimate the perceived quality of underwater compressed images, balance the relationship between the compression ratio and compression quality, reduce the pressure on the data cache, and thus improve the efficiency of the underwater image communication system.
