Blind Video Quality Assessment With Weakly Supervised Learning and Resampling Strategy
Blind Video Quality Assessment With Weakly Supervised Learning and Resampling Strategy
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DOI：
10.1109/tcsvt.2018.2868063
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发表时间：
2019-08
影响因子：
8.4
通讯作者：
Yu Zhang;Xinbo Gao;Lihuo He;Wen Lu;R. He
中科院分区：
文献类型：
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作者：
Yu Zhang;Xinbo Gao;Lihuo He;Wen Lu;R. He
由于自然视频的 3D 时空规律性和小规模视频质量数据库，有效的客观视频质量评估 (VQA) 指标很难获得，但却非常需要。在本文中，我们提出了一种通用的无参考 VQA 框架，该框架基于具有卷积神经网络 (CNN) 和重采样策略的弱监督学习。首先，通过弱监督学习训练八层 CNN，构建视频块 3D 离散余弦变换的变形与全参考（FR）VQA 指标判断的相应弱标签之间的关系。这样，CNN就获得了从FR-VQA度量转换而来的质量评估能力，并且可以通过训练后的网络提取失真视频的有效特征。然后，我们将根据训练网络预测的质量得分向量计算出的频率直方图映射到感知质量上。特别是，为了提高映射函数的性能，我们转移失真图像和视频的频率直方图以对训练集进行重新采样。实验在几个广泛使用的 VQA 数据库上进行。实验结果表明，所提出的方法与一些最先进的 VQA 指标相当，并且具有良好的鲁棒性。
Due to the 3D spatiotemporal regularities of natural videos and small-scale video quality databases, effective objective video quality assessment (VQA) metrics are difficult to obtain but highly desirable. In this paper, we propose a general-purpose no-reference VQA framework that is based on weakly supervised learning with a convolutional neural network (CNN) and a resampling strategy. First, an eight-layer CNN is trained by weakly supervised learning to construct the relationship between the deformations of the 3D discrete cosine transform of video blocks and the corresponding weak labels judged by a full-reference (FR) VQA metric. Thus, the CNN obtains the quality assessment capacity converted from the FR-VQA metric, and the effective features of the distorted videos can be extracted through the trained network. Then, we map the frequency histogram calculated from the quality score vectors predicted by the trained network onto the perceptual quality. Especially, to improve the performance of the mapping function, we transfer the frequency histogram of the distorted images and videos to resample the training set. The experiments are carried out on several widely used VQA databases. The experimental results demonstrate that the proposed method is on a par with some state-of-the-art VQA metrics and has promising robustness.