Learning discriminant DCT coefficients driven block descriptor for digital dropout detection system in degraded archived media

作者：

Highlights：

• Identify a set of DCT coefficients that can be used in digital dropout error classification.

• A weighted neighborhood sampling strategy based on spatially correlated directional behavior.

• Feature extraction in DCT domain, resulting in lower time complexity and computational load.

• Correlates highly with human subjective judgments of quality of error.

摘要

•Identify a set of DCT coefficients that can be used in digital dropout error classification.•A weighted neighborhood sampling strategy based on spatially correlated directional behavior.•Feature extraction in DCT domain, resulting in lower time complexity and computational load.•Correlates highly with human subjective judgments of quality of error.

论文关键词：Digital dropout,Degraded media archive,DCT,Genetic algorithm,Edge detection,Support vector machine

论文评审过程：Available online 9 April 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.04.004