Dual-quality map based no reference image quality assessment using deformable convolution

On the basis of the convolutional neural network based on the absolute value layer, a convolutional neural network based on multi-level parallel is proposed. This model adds shortcut connections between different convolutional layers of the convolutional neural network. The shortcut connections allow the feature map output from the shallower convolutional layers in the network to be directly used as branch information, which is similar to the deep convolutional layer. The feature maps are combined to obtain a feature map with more comprehensive information. By using the features of the shallow convolutional layer and the deep convolutional layer at the same time, the embedded traces it learns are more accurate. The final experimental results prove that the multi-level parallel convolutional neural network has a significant improvement in detection performance compared with the rich model art creation analysis algorithm, even when confronting the HILL art creation algorithm with an embedding rate of 0.4bpp.

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Dual-quality map based no reference image quality assessment using deformable convolution

Cited by 3 publications

References 54 publications

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Quality assessment for multi-exposure fusion light field images with dynamic region segmentation

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