Paper Info
Title
Artifact Detection Maps Learned using Shallow Convolutional Networks
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.
Year
DOI
Venue
2018
10.1109/SSIAI.2018.8470369
2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)
Keywords
Field
DocType
VID-MAP,Artifacts,Natural Scene Statistics,Upscaling Detection,Combing Detection,Source Inspection
Computer vision,Software release life cycle,Task analysis,Pattern recognition,Convolution,Computer science,Feature extraction,Learning differences,Artificial intelligence,Combing,Nonlinear distortion,Detector
Conference
ISSN
ISBN
Citations 
1550-5782
978-1-5386-6569-5
0
PageRank 
References 
Authors
0.34
15
2
Name
Order
Citations
PageRank
Goodall, T.1183.72
Alan C. Bovik25062349.55