New enhancements to cut, fade, and dissolve detection processes in video segmentation

TLDR

The study focuses on accurately classifying and temporally locating fade‑in, fade‑out, and dissolve transitions, distinguishing itself from earlier work that only detected transition existence. The authors propose enhanced cut, fade, and dissolve detection algorithms, including an adaptive threshold to reduce noise artifacts and two‑step methods that eliminate false positives. They implement these algorithms and evaluate them against two standard shot‑detection techniques on a comprehensive dataset, showing superior performance. The evaluation demonstrates that the proposed enhancements improve detection accuracy and reduce false positives compared to existing methods.

Abstract

We present improved algorithms for cut, fade, and dissolve detection which are fundamental steps in digital video analysis. In particular, we propose a new adaptive threshold determination method that is shown to reduce artifacts created by noise and motion in scene cut detection. We also describe new two-step algorithms for fade and dissolve detection, and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from other early work in scene change detection which tends to focus primarily on identifying the existence of a transition rather than its precise temporal extent. We evaluate our improved algorithms against two other commonly used shot detection techniques on a comprehensive data set, and demonstrate the improved performance due to our enhancements.