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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2019.2933743
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Liu, Huanhua
|e verfasserin
|4 aut
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| 245 |
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|a Deep Learning based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression
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|c 2019
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND threshold for each pixel or sub-band separately, which may not reflect the total masking effect of a picture accurately. In this paper, we propose a deep learning based PW-JND prediction model for image compression. Firstly, we formulate the task of predicting PW-JND as a multi-class classification problem, and propose a framework to transform the multi-class classification problem to a binary classification problem solved by just one binary classifier. Secondly, we construct a deep learning based binary classifier named perceptually lossy/lossless predictor which can predict whether an image is perceptually lossy to another or not. Finally, we propose a sliding window based search strategy to predict PW-JND based on the prediction results of the perceptually lossy/lossless predictor. Experimental results show that the mean accuracy of the perceptually lossy/lossless predictor reaches 92%, and the absolute prediction error of the proposed PW-JND model is 0.79 dB on average, which shows the superiority of the proposed PW-JND model to the conventional JND models
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|a Journal Article
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| 700 |
1 |
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|a Zhang, Yun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Huan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fan, Chunling
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kwong, Sam
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kuo, C-C Jay
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fan, Xiaoping
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2019) vom: 13. Aug.
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|x 1941-0042
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| 773 |
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|g year:2019
|g day:13
|g month:08
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|u http://dx.doi.org/10.1109/TIP.2019.2933743
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