Parallel parsing of MPEG video

Video parsing refers to the detection and classification of abrupt and gradual scene changes in a video stream and constitutes an important preprocessing step in applications that treat video streams as sources of information. Parallel processing is proposed as a means of dealing with the high computational demands of video parsing. Parallel versions of two algorithms that detect scene transitions in compressed video streams are proposed. Three granularities of parallelism are investigated; Group of Pictures (GOP), frame and slice. Results show that the GOP-level implementation, which represents the coarsest granularity of task and data decomposition, always performs the best. The slice and frame levels of granularity take the second and third place respectively. The speedup a´s shown to be almost linear in the case of the GOP level of granularity, whereas the Synchronization overheads are seen to be high for the frame and slice levels of granularity.