本文介绍了一种新型的利用图像活动性(IAM)快速估计水下图像端到端体验质量的方法。首先引入结构相似度(SSIM)作为图像感知服务质量(PQo S)参数评价图像质量,将图像初始活动性(IAM)作为区分图像内容的本征参数;随后基于质量向量(QV)的概念,分别分析了非压缩图像的结构相似度,图像初实活动性与无条件丢失概率(SSIM-IAM.-ulp)之间的联合特性,以及压缩图像的结构相似性,压缩率与无条件丢失概率(SSIM-IAM.-ulp)之间的联合特性。最后,在上述联合特性的基础上提出了劣化图像SSIM的预测算法。测试实验证明,该类算法有较高的预测准确率,预测误差最低可达0.8%。This paper introduces a novel method for quick estimating the end to end Perceived Quality of Service( PQo S) of underwater image based on image activity measure( IAM). Structure Similarity( SSIM) is first introduced as the evaluation standard of image PQo S parameter to evaluate the quality of image,while the IAM of original image is utilized as an intrinsic parameter to discriminate the image contents. Then,based on the concept of quality vector( QV),the SSIM-ulp-IAM0 features for non-compressed picture are analyzed,and the SSIM-CR-ulp features for compressed picture are analyzed as well. On this basis,the prediction algorithm based on SSIM for the degraded image is proposed. Test experiment result shows that this kind of prediction algorithm has high accuracy rate,and the lowest prediction error reaches to 0. 8%.国家自然科学基金面上(61571377;61471308)项目资
