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• W2906365278 abstract "Automatically identifying the locations and severities of video artifacts without the advantage of an original reference video is a difficult task. We present a novel approach to conducting no-reference artifact detection in digital videos, implemented as an efficient and unique dual-path (parallel) excitatory/inhibitory neural network that uses a simple discrimination rule to define a bank of accurate distortion detectors. The learning engine is distortion-sensitized by pre-processing each video using a statistical image model. The overall system is able to produce full-resolution space-time distortion maps for visualization, providing global distortion detection decisions that represent the state of the art in performance. Our model, which we call the video impairment mapper (VIDMAP), produces a first-of-a-kind full-resolution map of artifact detection probabilities. The current realization of this system is able to accurately detect and map eight of the most important artifact categories encountered during streaming video source inspection: aliasing, video encoding corruptions, quantization, contours/banding, combing, compression, dropped frames, and upscaling artifacts. We show that it is either competitive with or significantly outperforms the previous state of the art on the whole-image artifact detection task. A software release of VIDMAP that has been trained to detect and map these artifacts is available online: http://live.ece.utexas.edu/research/quality/VIDMAP_release.zip for public use and evaluation." @default.
• W2906365278 created "2019-01-01" @default.
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• W2906365278 date "2019-06-01" @default.
• W2906365278 modified "2023-10-02" @default.
• W2906365278 title "Detecting and Mapping Video Impairments" @default.
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• W2906365278 doi "https://doi.org/10.1109/tip.2018.2889276" @default.
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