A Statistical Framework to Enlarge the Potential of Digital TV Broadcasting Maria Teresa Andrade, Artur Pimenta Alves INESC Porto/FEUP Porto, Portugal
Aims of the work use statistical multiplexing for multi-channel TV systems with the ability of anticipating bit rate behaviour of individual channels to obtain: Efficient use of bandwidth anticipate amount of unused bandwidth and re-allocate to other existing services include additional datacasting or video services using the same resources Reduce costs Higher picture quality Increased programming/services choice
The context DTV scenario moving to all digital world with seamless integration with the Internet interactivity (local and remote) possibility of datacasting additional services demand for higher quality and more choice demand for HDTV programmes Still remaining problem bandwidth is still an expensive resource high-quality still requires high-bandwidth CBR encoding not efficient in the trade-off quality/bandwidth transmission channels usually of fixed bit rate broadband connections introduced rather slowly existing stat mux not very efficient
Statistical multiplexing present situation versus advanced solution Encoders co-located to the multiplexer Restricted VBR mode with feedback loop No guarantees of seamless constant quality Released bandwidth redistributed within the existing services in the multiplex Remote encoder operation True VBR operation Know in advance expected bit rate requirements Define a-priori maximum and minimum expected bandwidth gains Include on-the-fly new services within the multiplex
Possible approaches regarding bit rate constant bit rate with number of bits spent in each GOP controlled to a mean value - varying picture quality or distortion variable bit rate controlling the distortion - constant quality sequences constant bit rate using always the highest bit rate needed to satisfy at all times a minimum level of distortion
The probabilistic framework Statistical framework using bayesian inference to describe occurrence of valleys in the bit rate likelihood of valleys Statistical framework using bayesian Weibull survival model to analyse duration of valleys probability that the duration of valleys exceeds a certain time t Numerical sampling methods (MCMC) to obtain posteriori distributions predict occurrence and duration of valleys
Statistical framework Basis Video sources with almost-constant and high picture quality VBR operation Sources analysed on a GOP and scene-basis Analysis of a comprehensive number of sources Build data base with characteristics extracted from sources: Degree of criticality (difficulty to encode) and variability Type of content Parameters of a family of probabilistic models Assign and use posteriori pdfs for real sources in a statistical mux; anticipate released bandwidth to include extra services
Core of the work build classification matrix (genre, profile, quality) build statistical framework with Bayesian inference techniques obtain statistical characterisation of VBR video sources infer family of probabilistic models capable of adequately describing the video sources, incorporating prior knowledge obtained from sets of training data predict the amount of unused bandwidth in respect to a specified mean value using the posteriori probabilities (predict occurrence and duration of valleys )
Classifying sources Cover whole spectrum of TV programming Use DVB category classification for genres Encode with two different quantisation step sizes obtaining two different quality levels Fair and Good Obtain statistics (mean and variance) per quality level mouvement + detail (activity) and criticality
Characterising the traffic Granularity limited to GOPs (0,5 s) and to scenes: Human eye can t perceive variations in quality with durations less than 1 s Mechanisms that (re) allocate bandwidth are not able to react faster Inside a scene the type of content remains essentially the same, therefore little variation in bit rate New scenes always start with a new GOP
Influence of content bit rate follows a pdf (best fit Weibull, Gamma or Beta) at instante t i there is a given probability θ that the GOP bit rate be above or bellow a certain threshold. Video source Same type of content Encoder (generation of bits) generation of bits per GOP varies with content probability of ocurrence of valleys will also differ θ 1-θ above bellow Video sources Different type of content Encoder (generation of bits) θ i 1-θ i above bellow Influence (type of content)
Problem formulation Knowing that bitstream X has a number of characteristics, which is the probability of expecting a given bit rate behaviour, or the occurrence of valleys, throughout its duration? valley -> a random variable with two possible states observations -> specific characteristics -> prior knowledge as a pdf model selection (maximizing a pdf) -> posteriori pdf -> state of random variable
Bayes analyses Unknown parameter: GOP dev Fix a prior on the unknown parameter: p(gop dev ) Collect and observe the data: D = {X 1, X 2,..., X N } Calculate the posteriori distribution p(valleys) knowing the data X
Statistical model an unknown parameter: GOP dev, the deviation of the GOP bit rate in respect to the expected mean bit rate and a random variable: Occurrence of a valley in the bit rate, with 2 possible states: S = 1 a valley has occurred (GOP bit rate less than a certain threshold) S = 0 a valley did not occurred
Bayes analyses Sequence of scenes stream of GOPs θ Bellow (green light) 1-θ Above (red light) Uncertain variable or parameter, θ = GOP dev (variability of GOP bit rate) Prior distribution of parameter, p(θ ξ) Set of observations D = {X 1, X 2,..., X N } = stream of GOPs Having observed N GOPS, how to predict the value of occurrence N+1? Will it be a valley in the GOP bit rate (green light) or not (red light)? p(x N+1 θ, ξ) =? knowing p(x N+1 θ, ξ) and p(θ ξ) Use Bayes rule, average over the possible values of θ and use the expansion probability rule.
Bayes analysis Bayes rule to determine posteriori distribution of θ given the D set of GOPs and background knowledge average over the possible values of θ, using the expansion rule: p(x N+1 = green D, ξ) = p(x N+1 = green θ, ξ). p(θ D, ξ) dθ = θ. p(θ D, ξ) dθ E p(θ D, ξ )(θ) = = = = + θ ξ θ ξ θ ξ θ θ ξ θ ξ ξ θ ξ θ ξ θ d p D p D p a b D p D p D p p D p N b a b a ) ( ), ( ) ( ) (1 ), (,and ) ( ), ( ) ( ), ( ; red lights number of ; green lights number of
Bayes analyses - remarks prior p(gop dev ) is only an approximation obtained posteriori p(valleys) will also be approximation Important to carefully analyse the data, select the priors and test/calculate the posterioris for a great number of priors Choosing the prior: Kolmogorov method (minimum distance) Maximum likelihood Bayes Test/calculate the posterioris: Simulation methods through Markov Chain (MC Monte Carlo)
Building the framewok Select diferent types of content covering major typical TV programming build complexity matrix with classes Encode in VBR / near-constant quality Analyse and extract characteristics on a GOP and scenebasis ocurrence of scene changes, length of scenes bit rate per image, GOP and scene criticality (number of bits per pixel for a given quality) variability (peak-to-mean ratio, coeficient of variation, autocorrelation) valleys in bit rate within GOPs and scenes (intensity, variance, distance between valleys, length of valleys) Obtain the best priors Conduct calculations (using MCMC) to obtain posterioris
Building blocks of the VBR encoder framework Video analyser (objective measures and subjective classification) Classes and characteristics database Bayesian inference Probabilistic distributions database
Functional blocks Statistical Bayesian framework Good / high quality video sources (MPEG-2/4 CBR or VBR) VBR transcoder prior selection Posteriori calculations Viewer Subjective classifier Database Statistics, classes analyser Extract statistics Database VBR /constant quality sequences Advanced statistical mux Aditional services (datacasting, scalable video coders, etc) Database pdf families, parameters, classes Increased-value multiplex
Using the framework Analyse VBR video source (at start and periodically every 5 s) -> classify / update classification and select model Calculate the amount of probable bit rate that will be available Estimate occurrence and duration of next valley
Initial measures occurrences per % of mean GOP rate - film "Gladiator" number of occurrences 200 180 160 140 120 100 80 60 40 20 0 1,15 1,1 1,05 1 0,95 0,9 0,85 0,8 0,75 0,5 % of mea n 80% of GOP ocurrences within +/- 5% of mean 16% of GOP occurrences are bellow 5% mean (green light) 4 % of GOP occurrences are above mean (red light)
Initial measures ocurrences per % of mean value - film on DVD number of GOPs 70 60 50 40 30 20 10 0 1,35 1,30 1,25 1,20 1,15 1,10 1,05 1,00 0,95 0,90 0,85 0,80 0,75 0,70 0,65 % of mean GOP rate 18% of GOP ocurrences within +/- 5% of mean 50% of GOP occurrences are bellow 5% mean (green light) 32 % of GOP occurrences are above mean (red light)
Initial measures ocurrences per % of mean value - film on DVD number of GOPs 160 140 120 100 80 60 40 20 0 1,30 1,25 1,20 1,15 1,10 1,05 1,00 0,95 0,90 0,80 0,75 % of mean GOP rate 70% of GOP ocurrences within +/- 5% of mean 20% of GOP occurrences are bellow 5% mean (green light) 10 % of GOP occurrences are above mean (red light)
Initial measures ocurrences per % of mean value - film on DVD number of GOPs 60 50 40 30 20 10 0 1,40 1,35 1,30 1,25 1,20 1,15 1,10 1,05 1,00 0,95 0,90 0,85 0,80 % of mean GOP rate 24% of GOP ocurrences within +/- 5% of mean 52% of GOP occurrences are bellow 5% mean (green light) 24% of GOP occurrences are above mean (red light)
Initial measures ocurrences per % of mean value - film on DVD number of GOPs 120 100 80 60 40 20 0 1,25 1,20 1,15 1,10 1,05 1,00 0,95 0,90 0,85 0,80 % of mean GOP rate 60% of GOP ocurrences within +/- 5% of mean 28% of GOP occurrences are bellow 5% mean (green light) 12% of GOP occurrences are above mean (red light)
Statistical measures
Statistical measures
Statistical measures
Statistical measures
Statistical measures
Statistical measures
Statistical measures
Thank you so much for your attention!