Journal of VLSI Signal Processing Systems
Adaptive multi-resource prediction in distributed resource sharing environment
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
GRACE-1: Cross-Layer Adaptation for Multimedia Quality and Battery Energy
IEEE Transactions on Mobile Computing
Energy-efficient CPU scheduling for multimedia applications
ACM Transactions on Computer Systems (TOCS)
Complexity-Constrained Video Bitstream Shaping
IEEE Transactions on Signal Processing
Rate-distortion-complexity modeling for network and receiver aware adaptation
IEEE Transactions on Multimedia
Object segmentation and labeling by learning from examples
IEEE Transactions on Image Processing
Power-minimized bit allocation for video communication over wireless channels
IEEE Transactions on Circuits and Systems for Video Technology
Evaluating MPEG-4 video decoding complexity for an alternative video complexity verifier model
IEEE Transactions on Circuits and Systems for Video Technology
H.264/AVC baseline profile decoder complexity analysis
IEEE Transactions on Circuits and Systems for Video Technology
Power-rate-distortion analysis for wireless video communication under energy constraints
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Complexity scalable motion compensated wavelet video encoding
IEEE Transactions on Circuits and Systems for Video Technology
Proceedings of the 3rd workshop on Mobile video delivery
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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.