Efficient video coding using visual sensitive information for HEVC coding standard

Pallab Kanti Podder, Manoranjan Paul, Manzur Murshed

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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The latest High Efficiency Video Coding (HEVC) standard introduces a large number of inter-mode block partitioning modes. The HEVC reference test model (HM) uses partially-exhaustive tree-structured mode selection which still explores a large number of prediction unit (PU) modes for a coding unit (CU). This impacts on encoding time rise which deprives a number of electronic devices having limited processing resources to use various features of HEVC. By analyzing homogeneity, residual and different statistical correlation among modes, many researchers speed-up the encoding process through the number of PU mode reduction. However, these approaches could not demonstrate similar rate-distortion (RD) performance with the HM due to their dependency on existing Lagrangian cost function (LCF) within the HEVC framework. In this work, to avoid the complete dependency on LCF in the initial phase, we exploit visual sensitive foreground motion and spatial salient metric (FMSSM) in a block. To capture its motion and saliency features, we use the dynamic background and visual saliency modeling respectively. According to the FMSSM values, a subset of PU modes is then explored for encoding the CU. This preprocessing phase is independent from the existing LCF. As the proposed coding technique further reduces the number of PU modes using two simple criteria (i.e. motion and saliency), it outperforms the HM in terms of encoding time reduction. As it also encodes the uncovered and static background areas using the dynamic background frame as a substituted reference frame, it does not sacrifice quality. Tested results reveal that the proposed method achieves an average of 32% encoding time reduction of the HM without any quality loss for a wide range of videos.
Original languageEnglish
Pages (from-to)75695 - 75708
Number of pages14
JournalIEEE Access
Publication statusPublished - 29 Nov 2018


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