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- W2893929099 abstract "Automatically identifying the locations and severities of video artifacts is a difficult problem. We have developed a general method for detecting local artifacts by learning differences between distorted and pristine video frames. Our model, which we call the Video Impairment Mapper (VID-MAP), produces a full resolution map of artifact detection probabilities based on comparisons of exitatory and inhibatory convolutional responses. Validation on a large database shows that our method outperforms the previous state-of-the-art. A software release of VID-MAP that was trained to produce upscaling and combing detection probability maps is available online: http://live.ece.utexas.edu/research/quality/VIDMAP release.zip for public use and evaluation." @default.
- W2893929099 created "2018-10-05" @default.
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- W2893929099 date "2018-04-01" @default.
- W2893929099 modified "2023-10-14" @default.
- W2893929099 title "Artifact Detection Maps Learned using Shallow Convolutional Networks" @default.
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- W2893929099 doi "https://doi.org/10.1109/ssiai.2018.8470369" @default.
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