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

1
Content Based MPEG Video Traffic Modeling Ali 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
Survey..Classic Modeling

• Non MPEG
  
• 
  
• Maglaris
  
• Sen
• Grunenfelder
• Heyman
• Hughes
• shim

• MPEG
• Pancha
• Heyman
• Wu
• Krunz
• Ni

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)
• k1,2..N is the kth shot in a clip of N shots,
  i1,2..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) models parameters for the
  kth shot6.
• 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 a2 and ß70.
• 3. According to the type of the shot, use the
  table to calculate the auto-regressive (AR)
  models 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 models traffic has
  been fed into an ATM multiplexer with network
  loads of 70 and 90.

16
(No Transcript)
17
Network Performance
18
Limitations

1. Image representations based on low-level visual
   primitives such as texture, and motion.
2. The determination of shots is also a complex
   task.
3. 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.