Content Based MPEG Video Traffic Modeling Ali M. Dawood and Mohammed Ghanbari, senior member, IEEE

1. Content Based MPEG Video Traffic ModelingAli M. Dawood and Mohammed Ghanbari, senior member, IEEE Presented by Premchander Reddy & Lakshmi deepthi Pasupuleti To Donald Adjeroh As a partial requirement for course CS558

2. What is video modeling? Video model is an aid for designing and testing future communication networks that will carry multiplexed video traffic. It is an essential tool in estimating many networking issues such as the delay arising from statistical multiplexing and the bandwidth required for carrying video

3. Non MPEG Maglaris Sen Grunenfelder Heyman Hughes shim MPEG Pancha Heyman Wu Krunz Ni Survey…..Classic Modeling

4. Classic Modeling • In the classical modeling the mean and variance of real video are matched to an AR ( Auto regressive) model or any known distribution function. The nature of the video content and the length of the video is not taken into consideration here. • But modeling a video considering its nature and content can obviously result in better representation of video.

5. Introduction: Content Based Modeling • Decomposition of video Video Clip : Such as a Film Stories : Such as News session Shots : Continuous action GOP : Group of Pictures Video Frames : I P B frames

6. Shot Classification • Shot is a homogeneous video • Modeling of video should start from modeling of shot. • Texture and Motion are used to classify shots into groups • 3 levels of texture and 3 levels of motion are chosen • The levels are namely LL LM LH ML MM MH HL HM HH • L M H stand for Low Medium and High respectively

7. Measuring the Texture and Motion • Texture: The average magnitudes of the DCT coefficients of luminance/block for each frame is calculated and then averaged over the shot. • Motion: The magnitude of motion vectors/macro block are extracted for each frame type and then averaged over the shot.

8. The relation between average DCT coefficient and bit rate is distinct for the I-frame. • Due to motion it is not so distinct for P and B frames. • So we take the texture information from the I-frames. • The motion information Is taken from P and B frames since I frame is intra-frame coded. • I frames are combined with those of P and B frames for a reliable classification. • For example the classification of texture can be known from I frames and motion-based classification is known from P and B frames.

9. Characterization of Real Video • The shot classification were applied to a 30 min BBC news bulletin. • The frequencies of occurrence of each shot type was tabulated. • The transition probability table was also tabulated. • Transition probability table gives us the probability of a particular shot type following the next type.

10. Composition of Video Clips • Mean bit rate is calculated for each shot type and is divided into I,P,B frames. • After classification of shots and determination of bit rate, proportion of I,P,B bit rates, the shot can be defined as a vector. Sk(AR_Ii, AR_Pi, AR_Bi, tk) k=1,2..N is the kth shot in a clip of N shots, i=1,2..9 is the ith shot type,tk is the duration of the kth shot.

11. Summary of synthetic generation of CBM • 1. Define the number of shots(N) in the video clip. • 2. Specify the shot type and derive the mean bit rate of each shot type, and derive the mean bit rate of each shot from the overall mean bit rate. • 3. Specify the shot duration, according to the statistics and Gamma function. • 4. Using the mean and variance, calculate the auto-regressive (AR) model’s parameters for the kth shot[6]. • 5. Go to step 3 for the kth + 1 shot.

12. Results from Simulation of Deterministic CBM • The performance of the proposed model was tested against a real video clip. • A virtual video clip was edited from 11shots and the proposed model was applied. • It was observed that the CBM traffic closely follows the real non homogenous MPEG traffic.

13. Realistic CBM • Since the deterministic CBM is based on subjective description of the video content, the shot classification may vary from person to person. • In order to derive a more realistic content based video model the transition and the durations are made probabilistic, based on the shot characteristics. • A new shot type transition probability table is formulated. • A nine-state model is used to represent the probabilistic CBM.

14. Summary of Probabilistic CBM • 1.Start from an initial state. • 2. Find the duration of the slot with a gamma function of α=2 and β=70. • 3. According to the type of the shot, use the table to calculate the auto-regressive (AR) model’s parameters. • 4. Run AR model for the duration of the shot given in step 2. • 5. Transit to the next state according to the new transition table. • 6. Go to step 2.

15. Comparison of Results • A 2 min video clip was modeled with a classical AR method, deterministic CBM and probabilistic CBM. • The worst performance was observed for the classical method which do not consider video content. • The best performance was observed for deterministic CBM . • The purely statistical probabilistic CBM had much better performance than the classical model. • The network performance with these traffics was also evaluated, where each model’s traffic has been fed into an ATM multiplexer with network loads of 70% and 90%.

17. Network Performance

18. Limitations • Image representations based on low-level visual primitives such as texture, and motion. • The determination of shots is also a complex task. • Different people have different visual perceptions, so classification of shots based on color, texture and motion becomes a problem.

19. Suggested Improvements • The classification of shots based on contextual information such as appearance of an anchor in a video can be useful. • This type of classification is easy as the contextual information from which the classification is done is viewed as the same by all the people.