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Statistical framework for video decoding complexity modeling and prediction

Kontorinis, N; Van Der Schaar, M; Andreopoulos, Y; (2009) Statistical framework for video decoding complexity modeling and prediction. IEEE Transactions on Circuits and Systems for Video Technology , 19 (7) 1000 -1013. 10.1109/TCSVT.2009.2020256. Green open access

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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.

Title:Statistical framework for video decoding complexity modeling and prediction
Open access status:An open access version is available from UCL Discovery
Publisher version:http://dx.doi.org/10.1109/TCSVT.2009.2020256
Additional information:© 2009 IEEE. Personal use of this material (accepted version) is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Electronic and Electrical Engineering

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