基于机器学习的盲视觉质量评估与内容感知数据划分

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI:10.1109/NEUREL.2018.8587018

A. Gavrovska, G. Zajic, M. Milivojević, I. Reljin

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引用次数: 1

摘要

多年来，人们提出了不同的基于机器学习的图像质量评估模型。在本文中，我们分析了数据分区。由于统计数据分区可能会由于迭代次数而影响结果，因此我们分析了内容感知分区的效果。使用公开可用的数据集和不同的平均意见得分对不同的划分方法和模型进行了分析。

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Machine-learning based Blind Visual Quality Assessment with Content-aware Data Partitioning

Over the years different machine-learning based image quality assessment models have been proposed. In this paper, we analyze data partitioning. Since statistical data partitioning may affect the results due to the number of iterations, we analyze the effect of content-aware partitioning. The results are analyzed for different partitioning methods and models using publicly available dataset and difference mean opinion scores.

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2018 14th Symposium on Neural Networks and Applications (NEUREL)

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