Video

Video is an electronic medium for the recording, copying and broadcasting of moving visual images.

Model Type

• Continuous Normal VBR – This model is the simplest of all models. It uses Normal Distribution for the generation of bits per pixel. In this model, consecutive packet sizes are independent of each other.
  • Frames per second – Number of frames arriving per second. This is in the range of 10 – 50.
  • Pixels per frame -Number of pixelsin each frame. This is in the range of 10000 – 100000.
  • Bits per pixel (µ)– Mean value of the normal distribution used to generate the value of bits per pixel.
  • Bits per pixel (Σ) – Standard Deviation of the normal distribution used to generate the value of bits per pixel.
• Continuous State Autoregressive Markov –This model incorporates the autocorrelation between the frames. Also, current packet size depends on the previous packet size via the first order autoregressive Markov process.
  • Frames per second – Number of frames arriving per second. This is in the range of 10 – 50.
  • Pixels per frame - Number of pixels in each frame. This is in the range of 10000 – 100000.
  • Constant A– First order autoregressive Markov processλ(n) can be generated by the recursive relation λ(n)=aλ(n-1)+bw(n).
  • Constant B– First order autoregressive Markov process λ(n) can be generated by the recursive relation λ(n)=a λ(n-1)+bw(n).
  • Eta– The steady-state average E(λ)and discreet auto covariance C(n) are given by E(λ) = ( b / (1-a) ) η C(n)=(b2/(1-a2))an where η is the Gaussian parameter.
• Quantized State Continuous Time Markov –In this model the bit rate is quantized into finite discrete levels. This model takes uniform quantization step as A bits/pixel. There are M + 1 possible levels (0, A, ….., MA).Transitions between levels are assumed to occur with exponential rates that may depend on the current level. This model is approximating the bit rate by a continuous time process λ(t) with discrete jumps at random Poisson time.
  • Frames per second – Number of frames arriving per second. This is in the range of 10 – 50.
  • Pixels per frame - Number of pixels in each frame. This is in the range of 10000 – 100000.
  • No of Multiplexed Sources– This model considers the aggregate instantaneous input rate λN(t) instead of the single source bit rate λ(t). The total rate is the sum of N independent random processes each with mean E(λ) and variance C(0) at steady state. Therefore, the steady state- mean of λN(t) will be E(λ N)=N x E(λ) bits/pixel.
  • Quantization Level– This model takes uniform quantization step as A bits/pixel. There are M + 1 possible levels (0, A, ….., MA). Transitions between levels are assumed to occur with exponential rates that may depend on the current level.
• Simple IPB Composite Model–In this model, the frames are organized as IBBPBBPBBPBBIBBPBB… i.e., 12 frames in a Group of Pictures(GOP).Generate X0 from a Gaussian distribution N(0, y 0).Set initial value N0= 0, D0 = 1.

For k = 1, 2,…, N-1, calculate Φkj , j = 1, 2,…,k iteratively using the following formulae

Nk = r(k) – j=1Σk-1Φk-1,j r(k-j)

Dk = Dk-1 –(N2k-1/Dk-1)

Φkk = Nk / Dk

Φkj = Φk-1, j-ΦkkΦk-1,k-j j=1,….,k-1

mk = j = 1ΣkΦkjXk-j

y k = (1- Φ2kk) yk-1

Finally, each Xk is chosen from N(mk, y k). Thus we get a process X with ACF approximating to r(k).

The auto correlation function can be calculated in a recursive way as follows:

r(0) = 1, r(k+1) = ((k+d)/(k+1))r(k)

Where d= H-0.5.

H is called the Hurst parameter k-β is used as the ACF of a self-similar process.We get the value of H parameter for a self-similar process using the relationship,

Β = 2 – 2H

Distribution of these data is Gaussian. For data to be Beta distributed, the following mapping is being used.

Yk = F-1β (FN(Xk)), k>0

Xk :Self-similar Gaussian process,

FN :The cumulative probability of normal distribution,

F-1β: The inverse cumulative probability functions of the Beta model.

• • Frames per second – Number of frames arriving per second. This is in the range of 10 – 50.
  • Hurst I–Refer i-button help of Simple IPB Composite Model.
  • Hurst B– Refer i-button help of Simple IPB Composite Model.
  • Hurst P– Refer i-button help of Simple IPB Composite Model.
  • Gamma I – Refer i-button help of Simple IPB Composite Model.
  • Gamma B– Refer i-button help of Simple IPB Composite Model.
  • Gamma P– Refer i-button help of Simple IPB Composite Model.
  • Eta I – Refer i-button help of Simple IPB Composite Model.
  • Eta B – Refer i-button help of Simple IPB Composite Model.
  • Eta P– Refer i-button help of Simple IPB Composite Model.