Adaptive video-dissolve detection method based on correlation between successive scenes

Abstract

We establish the importance of correlation between successive scenes in dissolve detection and propose a new adaptive dissolve detection method based on the analysis of a dissolve modeling error, which is the difference between an ideally modeled dissolve curve with no correlation and an actual dissolve curve including correlation. The proposed method consists of two steps. First, candidate dissolve regions are extracted using the characteristics of a downward-convex parabola; then each candidate region is checked using the dissolve modeling error. If the dissolve modeling error for a candidate region is less than a threshold equal to the dissolve modeling error with a target correlation, the candidate region is identified as a dissolve region with a lower correlation than the target correlation. The threshold is determined adaptively from the variance at the start and end of the candidate region and the given target correlation. By considering the correlation between successive scenes, the proposed method is able to function as a semantic scene-change detector. The proposed algorithm was tested on various types of data, and its performance proved to be more accurate and reliable than that of other, commonly used methods.