Open AccessFluctuation-Based Fade Detection for Local Scene Changes
Author(s) -
Sangho Yoon,
Chanhee Lee,
Ho Sub Lee,
Young Hwan Kim,
Seokhyeong Kang
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3125731
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In recent years, fade detection algorithms can classify fade scenes in massive video libraries have been developed. However, these algorithms misclassify some non-fade scenes as fade scenes, especially dissolve scenes and scenes with captions or flashing light sources. This paper proposes a new fade detection algorithm that uses similarity tendencies of luminance transitions to overcome such obstacles. To prevent detection accuracy degradation by letterboxing and captions, video frames are simplified. Then, fade candidates are detected by transition boundary detection using the angular and curvature characteristics of the luminance vectors. Finally, luminance flipping detection improves the detection accuracy by extracting the luminance retrograde phenomenon that occurs with flashing or light source movements. Through objective evaluation using $F_{1}$ score, the detection accuracy of the proposed algorithm was 0.884, which is an increase of 0.187 (21.2% improvement) compared with the average $F_{1}$ score of existing high-performance methods.