图像质量评估模型的仿真研究

摘要

研究了图像质量评估精度问题.针对图像采集,压缩传输中出现的畸变,在视觉上的感受特征不清,导致了图像效果差,难以识别图像特征.提出了一种全新的针对JPEG编码压缩并利用基于局部特征和分割的无参照模型图像质量评价模型,给出了对训练和测试样本集设置分割阈值的新方法.首先通过粒子群优化算法(PSO)方法对图像进行分割,然后针对不同区域,计算不同特征值,最后依据特征值计算整幅图像所对应的质量评估值.方法具有效率高,适用图像广的特性.实验显示改进方法所获得的图像质量评估值更接近人眼视觉判断值,有效地提高了图像识别的精度和准确度.%The perceived image distortion of any image is strongly depended on the local features, such as edge, flat and texture. A new objective no -reference (NR) image quality evaluation model based on local features and segmentation for JPEG coded image is presented in our previous paper, which was easy to calculate and applicable to various image processing applications. But the algorithmic thresholds investigation of the segmentation algorithm was not sufficient. Therefore in this paper, we want to investigate the suitable threshold values of our segmentation algo-rithm for both training and test images by the optimization method. Our experiments on various image distortion types indicate that its performance is significantly better than the conventional model.