Fast video encoding based on random forests View Full Text


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Article Info

DATE

2019-02-05

AUTHORS

Muhammad Tahir, Imtiaz A. Taj, Pedro A. Assuncao, Muhammad Asif

ABSTRACT

Machine learning approaches have been increasingly used to reduce the high computational complexity of high-efficiency video coding (HEVC), as this is a major limiting factor for real-time implementations, due to the decision process required to find optimal coding modes and partition sizes for the quad-tree data structures defined by the standard. This paper proposes a systematic approach to reduce the computational complexity of HEVC based on an ensemble of online and offline Random Forests classifiers. A reduced set of features for training the Random Forests classifier is proposed, based on the rankings obtained from information gain and a wrapper-based approach. The best model parameters are also obtained through a consistent and generalizable method. The proposed Random Forests classifier is used to model the coding unit and transform unit-splitting decision and the SKIP-mode prediction, as binary classification problems, taking advantage from the combination of online and offline approaches, which adapts better to the dynamic characteristics of video content. Experimental results show that, on average, the proposed approach reduces the computational complexity of HEVC by 62.64% for the random access (RA) profile and 54.57% for the low-delay (LD) main profile, with an increase in BD-Rate of 2.58% for RA and 2.97% for LD, respectively. These results outperform the previous works also using ensemble classifiers for the same purpose. More... »

PAGES

1-21

References to SciGraph publications

  • 2016-08. Fast encoding algorithm for high-efficiency video coding (HEVC) system based on spatio-temporal correlation in JOURNAL OF REAL-TIME IMAGE PROCESSING
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2017-01. Fast inter-prediction mode decision algorithm for HEVC in SIGNAL, IMAGE AND VIDEO PROCESSING
  • 2016-06-10. HEVC early termination methods for optimal CU decision utilizing encoding residual information in JOURNAL OF REAL-TIME IMAGE PROCESSING
  • 2017-03-16. Fast CU size and prediction mode decision algorithm for HEVC based on direction variance in JOURNAL OF REAL-TIME IMAGE PROCESSING
  • 2017-04-19. A unified architecture for fast HEVC intra-prediction coding in JOURNAL OF REAL-TIME IMAGE PROCESSING
  • 2013-12. CU splitting early termination based on weighted SVM in EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11554-019-00854-1

    DOI

    http://dx.doi.org/10.1007/s11554-019-00854-1

    DIMENSIONS

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