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Patch selection for neural network based no-reference image quality assessment

机译:基于神经网络的无参考图像质量评估的补丁选择

摘要

The present disclosure relates to a method for image patch selection for training a neural network for image quality assessment. The method includes receiving an input image and extracting one or more image patches from the input image. The moment of the extracted image patches is measured. There is a decision to accept or decline the extracted image patches according to the measured moment. Additional image patches are extracted until a minimum number, Nmin, of extracted image patches are accepted. Alternatively, selection criteria are adjusted until the minimum number of extracted image patches are accepted. The selected image patches are input into a neural network with a corresponding image quality value of the input image, and the neural network is trained with the image patches and image quality value. Also provided is a method for image quality assessment using a neural network trained as set forth above.
机译:本公开涉及用于训练神经网络以用于图像质量评估的图像补丁选择的方法。该方法包括接收输入图像并从输入图像中提取一个或多个图像块。测量提取的图像补丁的时刻。决定根据测量的时刻接受或拒绝提取的图像块。提取其他图像补丁,直到接受最少数量的N min 。或者,调整选择标准,直到接受最少数量的提取图像补丁为止。将所选择的图像块输入到具有输入图像的相应图像质量值的神经网络中,并且利用图像块和图像质量值来训练神经网络。还提供了一种使用如上所述训练的神经网络进行图像质量评估的方法。

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