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Advanced image quality assessment approach using multiple quality measures with the artificial neural network data processing support

机译:先进的图像质量评估方法,在人工神经网络数据处理支持下使用多种质量度量

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摘要

This paper deals with the subjective and objective image quality evaluation. The demand of an accurate image and video objective quality assessment tool is extremely important in modern multimedia systems. Possible enhancement of the performance in existent image quality assessment approaches using multiple quality measures with the support of the artificial neural network data processing is proposed. The analysis results of the known quality measures and their suitability for the particular image or video quality assessment problem are presented. The most suitable measures are used to implement the novel image quality assessment tool using artificial neural network data processing. Optimization of the proposed model has been done in order to achieve as highest generalization feature of the model as possible. Performance of the implemented model for the image quality assessment has been evaluated using the database of distorted images and subjective image quality assessment results with respect to the Mean Opinion Score (MOS) obtained by the group of observers. It is shown that the proposed image quality assessment model can achieve high correlation with the subjective image quality ratings.
机译:本文涉及主观和客观图像质量评估。在现代多媒体系统中,对精确的图像和视频客观质量评估工具的需求极为重要。提出了在人工神经网络数据处理的支持下,使用多种质量度量来提高现有图像质量评估方法性能的可能。介绍了已知质量度量的分析结果及其对特定图像或视频质量评估问题的适用性。最合适的措施用于使用人工神经网络数据处理来实现新颖的图像质量评估工具。为了实现模型的最高泛化特征,已经对所提出的模型进行了优化。已使用失真图像的数据库和主观图像质量评估结果对观察者组获得的平均意见得分(MOS)评估了已实现模型的图像质量评估性能。结果表明,所提出的图像质量评估模型可以与主观图像质量等级实现高度相关。

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