All traffic models for MPEG-like encoded variable bit rate (VBR) video can be categorized into (i) data rate models (DRMs), and (ii) frame size models (FSMs). Almost all proposed VBR traffic models are DRMs. Since DRMs generate only data arrival rate, they are good for estimating average packet-loss and ATM buffer over-flowing probabilities, but fail to identify such details as percentage of frames affected. FSMs generate sizes of individual MPEG frames, and are good for studying frame loss rate in addition to data loss rate. Among three previously proposed FSMs: (i) one generates frame sizes for full-length movies without preserving GOP-periodicity; (ii) another generates frame sizes for full-length movies without preserving size-based video-segment transitions; and (iii) the third generates VBR video traffic for news videos from scene content description provided to it presupposing a proper segmentation. In this paper, we propose two segmentation techniques for VBR videos - (a) Equal Number of GOPs in all shot classes (ENG), and (b) Geometrically Increasing Interval Lengths for shot classes (GIIL). Each technique partitions the GOPs in the video into size-based shot classes. Frames in each class produce three data-sets one each for I-, B-, and P-type frames. Each of these data-sets can be modeled with an axis shifted Gamma distribution. Markov renewal processes model interclass transitions. We have used QQ plots to show visual similarity of model-generated VBR video data-sets with original data-set. Leaky-bucket simulation study has been used to show similarity of data and frame loss rates between model-generated videos and original video. Our study of frame-based VBR video revealed GIIL segmentation technique separates the I-, B-, and P- frames in well behaved shot classes whose statistical properties can be captured by Gamma-based models.