“Neural Pixel Error Detection” by Doggett, Wolak, Tsatsoulis and McCarthy
Conference:
Type(s):
Entry Number: 23
Title:
- Neural Pixel Error Detection
Presenter(s)/Author(s):
Abstract:
Current video quality control entails a manual review of every frame for every video for pixel errors. A pixel error is a single or small group of anomalous pixels displaying incorrect colors, arising from multiple sources in the video production pipeline. The detection process is difficult, time consuming, and rife with human error. In this work, we present a novel approach for automated pixel error detection, applying simple machine learning techniques to great effect. We use an autoencoder architecture followed by statistical post-processing to catch all tested live action pixel anomalies while keeping the false positive rate to a minimum. We discuss previous dead pixel detection methods in image processing, and compare to other machine learning approaches.
References:
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D. E. Rumelhart, G. E. Hinton, and R. J. Williams. 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1. MIT Press, Cambridge, MA, USA, Chapter Learning Internal Representations by Error Propagation, 318–362. http://dl.acm.org/citation.cfm?id=104279.104293
Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, Jie Chen, Zhaogang Wang, and Honglin Qiao. 2018. Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications. In Proceedings of the 2018 World Wide Web Conference (WWW ’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 187–196. https://doi.org/10.1145/ 3178876.3185996
