IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i19p3110-d1760565.html
   My bibliography  Save this article

Neural Network-Based Atlas Enhancement in MPEG Immersive Video

Author

Listed:
  • Taesik Lee

    (Department of Computer Engineering, Dong-A University, Busan 49315, Republic of Korea)

  • Kugjin Yun

    (Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea)

  • Won-Sik Cheong

    (Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea)

  • Dongsan Jun

    (Department of Computer Engineering, Dong-A University, Busan 49315, Republic of Korea)

Abstract

Recently, the demand for immersive videos has surged with the expansion of virtual reality, augmented reality, and metaverse technologies. As an international standard, moving picture experts group (MPEG) has developed MPEG immersive video (MIV) to efficiently transmit large-volume immersive videos. The MIV encoder generates atlas videos to convert extensive multi-view videos into low-bitrate formats. When these atlas videos are compressed using conventional video codecs, compression artifacts often appear in the reconstructed atlas videos. To address this issue, this study proposes a feature-extraction-based convolutional neural network (FECNN) to reduce the compression artifacts during MIV atlas video transmission. The proposed FECNN uses quantization parameter (QP) maps and depth information as inputs and consists of shallow feature extraction (SFE) blocks and deep feature extraction (DFE) blocks to utilize layered feature characteristics. Compared to the existing MIV, the proposed method improves the Bjontegaard delta bit-rate (BDBR) by −4.12% and −6.96% in the basic and additional views, respectively.

Suggested Citation

  • Taesik Lee & Kugjin Yun & Won-Sik Cheong & Dongsan Jun, 2025. "Neural Network-Based Atlas Enhancement in MPEG Immersive Video," Mathematics, MDPI, vol. 13(19), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:19:p:3110-:d:1760565
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/19/3110/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/19/3110/
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:gam:jmathe:v:13:y:2025:i:19:p:3110-:d:1760565. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.