A full reference algorithm for dropped frames identification in uncompressed video using genetic algorithm

Dropped frames identification in a given video stream is always a challenging task for research community due to required heavy computation. To preserve the quality of service during any processing over visual information, drop of frame is least desired. Many contemporary work identifies the frame drop in terms of full reference algorithm (where reference and distorted video streams are available for comparison), or reduced reference algorithm (where some information about reference video are available) or no reference algorithm (where information about reference video are not available). This paper presents a novel full reference heuristic approach using genetic algorithm which identifies the dropped frame indices in a given distorted video stream with respect to original video stream. The proposed algorithm efficiently identifies dropped frame indices even if reference video stream contains repeated frames and spatially distorted too with low or high spatial distortions. The proposed algorithm is simulated and tested with 12 video streams. Simulation results suggested that it is more efficient with a video stream having lesser repeated frames.