基于支持向量机和改进网格搜索算法的图像质量评价

Wei Liu, Qian Huang, Ming Wei
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引用次数: 3

摘要

为了改善失真类型图像峰值信噪比和结构相似度等对图像质量的影响,提出了一种基于支持向量机和改进网格搜索算法的图像质量评价方法。首先,利用改进的网格搜索算法寻找支持向量机的最优核参数。然后,将样本数据输入到改进的SVM中进行预测评价。最后,建立了图像质量评价模型。实验结果表明,该方法有效且易于实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Quality Evaluation Based on SVM and Improved Grid Search Algorithm
In order to improve the influence of distortion type image such as peak signal-to-noise ratio and structural similarity, an image quality evaluation method based on Support Vector Machine (SVM) and Improved Grid Search Algorithm is proposed. First, using Improved Grid Search Algorithm to find the optimal kernel parameters in SVM. Then, input the sample data into the improved SVM for predictive evaluation. Finally, an image quality evaluation model is established. According to the experimental results, the method of evaluating the image quality is efficient and simple to implement.
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