Coarse-to-Fine Dissolve Detection Based on Image Quality Assessment

Although many approaches have been proposed for video shot boundary detection, dissolve detection remains an open issue. For a dissolve, we could find that the video frames reveal a “clarity–blur–clarity” visual pattern. Accordingly, the image quality in the dissolve also reveals a “high–low–high” pattern. Based on the above observation, in this paper a novel coarse-to-fine dissolve detection approach based on image quality assessment is presented. Firstly, the normalized variance autofocus function is employed to calculate the image quality value for its good performance and the image quality feature curve is obtained. The grooves on the curve, which are monotone decreasing to a local minimum and then are monotone increasing to a normal value, are detected by using a simple threshold-based method and deemed as dissolve candidates. After obtaining the coarse results, some refined features are extracted from these dissolve candidates and the final dissolve detection is accomplished with the help of the support vector machine based on a new dissolve length normalization method. The experimental results show that the proposed method is effective.