基于多参数和极限学习机的图像质量评价方法研究

Research on Image Quality Assessment Method Based on Multi-parameter and Extreme Learning Machine

现有的图像质量评价算法大部分都是基于单个参数,评价不够全面。提出了基于多参数和极限学习机的图像质量评价方法,该方法融合了传统统计参数(PSNR)、基于结构相似度的参数(SSIM)和基于自然场景分析的参数(VIF和FSIM),将这些参数作为极限学习机的输入,拟合出这些特征参数和人类主观评价分值的内在关系,挖掘其内在规律。本方法和采用单独参数评价的算法进行对比分析,在TID图像质量评价库上的实验结果表明,该方法得到了比单独参数和BP方法更好的主观感知一致性。

Present image quality assessment methods are mostly based on single parameter,but they cannot assess the image comprehensively.This paper proposes image quality assessment method based on multi-parameter and extreme learning machine.Traditional statistical parameter(PSNR),parameter based on structural similarity(SSIM)and parameter based on natural scene analysis(VIF and FSIM)are fused.These parameters are taken as the inputs to the extreme learning machine and it can excavate the relationship and inherent law between these parameters and human subjective mean opinion score.The performance of the method in the paper and method based on single parameter and BP are compared in the TID database,the experiment results demonstrate that the method in the paper achieve better subjective perceived consistency.