Abstract
MPEG-4 AVC encoded video streams have been analyzed using video traces and statistical features have been extracted, in the context of supporting efficient deployment of networked and multimedia services. The statistical features include the number of scenes composing the video and the sizes of different types of frames, within the overall trace and each scene. Statistical processing has been performed upon the traces and subsequent fitting upon statistical distributions (Pareto and lognormal). Through the construction of a synthetic trace, based upon this analysis, our selections of statistical distribution have been verified. In addition, different types of content, in terms of level of activity (quantified as different scene change ratio) have been considered. Through modelling and fitting, the stability of the main statistical parameters has been verified as well as observations on the dependence of these parameters upon the video activity level.
Highlights
Multimedia services gradually become more complex and interactive, and they are increasingly based upon the retrieval, transfer and delivery of video
The methodology of our work includes: 1) The separation of the video into scenes based upon important changes in the size of the I frames of the included Group of Pictures (GoP) and the subsequent calculation of the scenes length in terms of frames; in addition the scene separation scheme is fitted into a Pareto distribution as suggested by previous works; 2) The estimation of the size of I and B frames in a) the overall trace, b) a specific scene and c) a Group of Pictures; in addition the frame size scheme are fitted into statistical distribution as suggested from previous best practices
Having established the main mechanism, we investigate the stability of the main parameters of the statistical distributions, considering different types of content, based on the level of movement as quantified through the scene ratio
Summary
Multimedia services gradually become more complex and interactive, and they are increasingly based upon the retrieval, transfer and delivery of video. The second option uses algorithmically generated traces based on the statistical properties of the actual traces The properties of such traces can support service providers to dimension the bandwidth requirements of their services and facilitate smooth cooperation with the underlying network. In our current work we investigate the dynamically-changing rate of encoded content focusing on the extraction of statistical properties of the videos through their corresponding video traces. Considering the volume of different types of frames (I and B) and available statistical distributions we endeavor to model the volume of the encoded content and produce synthetic traces. Our work is contributing to 1) the consolidated design of different video trace processing aspects including the statistical parameter extraction, the model creation and the scene separation, and 2) the streamlining and verification of the methodology using tools that extensively deployed in research laboratories (Matlab).
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