IDEAS home Printed from https://ideas.repec.org/a/igg/jmdem0/v12y2021i3p16-38.html
   My bibliography  Save this article

Motion Estimation Role in the Context of 3D Video

Author

Listed:
  • Vania Vieira Estrela

    (Universidade Federal Fluminense (UFF), Brazil)

  • Maria Aparecida de Jesus

    (Universidade Federal Fluminense (UFF), Brazil)

  • Jenice Aroma

    (Karunya University, India)

  • Kumudha Raimond

    (Karunya Institute of Technology and Sciences, India)

  • Sandro R. Fernandes

    (Instituto Federal de Educacao, Ciencia e Tecnologia do Sudeste de Minas Gerais, Brazil)

  • Nikolaos Andreopoulos

    (Technological Institute of Iceland, Iceland)

  • Edwiges G. H. Grata

    (Universidade Federal Fluminense (UFF), Brazil)

  • Andrey Terziev

    (TerziA, Bulgaria)

  • Ricardo Tadeu Lopes

    (Federal University of Rio de Janeiro (UFRJ), Brazil)

  • Anand Deshpande

    (Angadi Institute of Technology and Management, Belagavi, India)

Abstract

The 3D end-to-end video system (i.e., 3D acquisition, processing, streaming, error concealment, virtual/augmented reality handling, content retrieval, rendering, and displaying) still needs improvements. This paper scrutinizes the motion compensation/motion estimation (MCME) impact in the 3D video (3DV) from the end-to-end users' point of view deeply. The concepts of motion vectors (MVs) and disparities are very close, and they help to ameliorate all the stages of the end-to-end 3DV system. The high-efficiency video coding (HEVC) video codec standard is taken into consideration to evaluate the emergent trend towards computational treatment throughout the cloud whenever possible. The tight bond between movement and depth affects 3D information recovery from these cues and optimizes the performance of algorithms and standards from several parts of the 3D system. Still, 3DV lacks support for engaging interactive 3DV services. Better bit allocation strategies also ameliorate all 3D pipeline stages while being attentive to cloud-based deployments for 3D streaming.

Suggested Citation

  • Vania Vieira Estrela & Maria Aparecida de Jesus & Jenice Aroma & Kumudha Raimond & Sandro R. Fernandes & Nikolaos Andreopoulos & Edwiges G. H. Grata & Andrey Terziev & Ricardo Tadeu Lopes & Anand Desh, 2021. "Motion Estimation Role in the Context of 3D Video," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 12(3), pages 16-38, July.
    • Handle: RePEc:igg:jmdem0:v:12:y:2021:i:3:p:16-38
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJMDEM.291556
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jmdem0:v:12:y:2021:i:3:p:16-38. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.