A Fast Perceptual Surveillance Video Coding (PSVC) Based on Background Model-Driven JND Estimation

Gang WANG, Mingliang ZHOU*, Haiheng CAO, Bin FANG, Shiting WEN, Ran Wei

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

5 Citations (Scopus)

Abstract

Perceptual video coding (PVC) optimization has been an important video coding technique, which can be consistent with the perception characteristics of the human visual system (HVS). Currently, PVC schemes incorporating the just noticeable distortion (JND) model can obtain better performance gain in all PVC schemes. To further accelerate the JND computation for real-time video coding applications (e.g. surveillance video coding and conference video coding), this paper proposes a fast perceptual surveillance video coding (PSVC) scheme based on background model-driven JND estimation method. First, to utilize the surveillance scene characteristics, the computation complexity of JND estimation can be significantly decreased by reusing the content complexity of background regions. Then we apply the perceptive video coding scheme into the background modeling-based surveillance video codec. The proposed scheme adopts background modeling frame as background anchor. Experimental results show that the proposed scheme can yield remarkable time saving of 42.33% maximum and on average 34.76% with approximate bitrate reductions and similar subjective quality, compared to HEVC and other state-of-the-art schemes.

Original languageEnglish
Article number2155006
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume35
Issue number6
Early online date24 Dec 2020
DOIs
Publication statusPublished - May 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 World Scientific Publishing Company.

Keywords

  • background modeling
  • Just noticeable distortion
  • perceptual video coding
  • surveillance video coding

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