End-to-End Blind Image Quality Assessment Using Deep Neural Networks
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Cites background or methods or result from "End-to-End Blind Image Quality Asse..."
...Until recently, there has been limited effort towards end-to-end optimized BIQA using deep convolutional neural networks (CNN) [10], [11], primarily due to the lack of sufficient ground truth labels such as the mean opinion scores (MOS) for training....
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...gMAD competition results between DB-CNN and MEON [11]....
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...For synthetic distortions, inspired by previous works [11], [18], [20], we construct a large-scale pre-training set based on the Waterloo Exploration Database [19] and PASCAL VOC 2012 [21], where the images are synthesized with nine distortion types and...
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...Other methods [11], [18] take advantage of the known synthetic degradation processes (e....
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...Specifically, DB-CNN fails to disprove MEON [11] in (a), which reveal its weakness in...
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299 citations
Additional excerpts
...[41] proposed the MEON...
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281 citations
246 citations
Cites background from "End-to-End Blind Image Quality Asse..."
...[27] proposed a deeper network to learn distortion type and image quality simultaneously....
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225 citations
References
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"End-to-End Blind Image Quality Asse..." refers background or methods in this paper
...mization steps adopt the Adam optimization algorithm [51] with a mini-batch of 40....
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...Other parameters in Adam are set by default [51]....
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55,235 citations
"End-to-End Blind Image Quality Asse..." refers background in this paper
...[17] significantly increased the depth of DNN by stacking ten convolutional and two fully connected layers, whose architecture was inspired by the VGG16 network [10] for image classification....
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49,914 citations