Statistical framework for video decoding complexity modeling and prediction

Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time - feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding

Numerical and Experimental Investigation of the Morphology Development of Expansion Clouds by a Powder Jet Flow

Explosion suppression is often the preferred method of explosion attenuation in industry. The morphology development of suppression clouds is important for the design of necessary coverage of the product. This paper presents a numerical and experimental investigation of the growth of powder dispersion as it expands from a discharge nozzle. A Lagrangian stochastic particle-tracking approach and the RNG k-e turbulence model are adopted in the flow field solver for the dispersed and continuous phases. The flow fields coupled with the particle interactions are predicted. The dispersion characteristics of the expansion of the powder cloud through a pipe for short intervals of time are investigated. This was compared with (1) captured images from experiments, (2) experimental data, and (3) results of previous simulations. Particle positions along the jet are presented. The effects of flow rate on the development of the cloud and a comparison with experimental results are also presented. It is noted that the coverage of the powder cloud can be controlled by the flow rate of the jet, and the developing length of the cloud is more influenced by the flow rate of jet flow than the developing width. The good qualitative agreements achieved are useful for further optimisation of product design

Singular forces and point-like colloids in lattice Boltzmann hydrodynamics

We present a second-order accurate method to include arbitrary distributions of force densities in the lattice Boltzmann formulation of hydrodynamics. Our method may be used to represent singular force densities arising either from momentum-conserving internal forces or from external forces which do not conserve momentum. We validate our method with several examples involving point forces and find excellent agreement with analytical results. A minimal model for dilute sedimenting particles is presented using the method which promises a substantial gain in computational efficiency.Comment: 22 pages, 9 figures. Submitted to Phys. Rev.