Study of long-duration MPEG-trace segmentation methods for developing frame-size-based traffic models

Abstract

Texture and temporal variations in scenes, and peculiarities of MPEG compression algorithms result in very complex frame-size data sets for any long-duration variable bit rate (VBR) video. A major hurdle in capturing the statistical behavior of such a data trace can be removed by segmentation of all frames into an appropriate number of analytically characterizable classes. However, video-trace segmentation techniques, particularly those which also enable preserving periodicity of group of pictures (GOP) in the modeled data, are lacking in the literature. In this paper, we propose and evaluate few techniques for segmenting frame-size data sets in any long-duration video trace. The proposed techniques partition the group of pictures in a video into size-based groups called shot-classes. Frames in each shot-class have three data-sets––one each for intra (I-), bi-directional (B-), and predictive (P-) type frames. We have evaluated the performance of the proposed segmentation techniques by modeling each of I-, B-, and P-type frame in each shot-class by a Gamma distribution. Accuracy and usefulness of the proposed segmentation methods in building frame-size traffic models have been evaluated by QQ plots and the leaky-bucket simulation study. The results reveal that one of the segmentation techniques is very effective in characterizing the frame-size data behavior in a long-duration VBR video.