• Login
    View Item 
    •   DSpace Home
    • College of Engineering (CEN)
    • Department of Computer Science and Engineering
    • View Item
    •   DSpace Home
    • College of Engineering (CEN)
    • Department of Computer Science and Engineering
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Predicting split decisions of coding units in HEVC video compression using machine learning techniques

    Thumbnail
    View/ Open
    enc-CU-split-prediction-F17-v12-SpringerMultimedia.pdf (1.597Mb)
    Date
    2018
    Author
    Hassan, Mahitab Alaaeldin
    Shanableh, Tamer
    Advisor(s)
    Unknown advisor
    Type
    Article
    Postprint
    Peer-Reviewed
    Metadata
    Show full item record
    Abstract
    In this work, we propose to reduce the complexity of HEVC video encoding by predicting the split decisions of coding units. We use a sequencedependent approach in which a number of frames belonging to the video being encoded are used for generating a classification model. At each coding depth of the coding units, features representing the coding unit at that particular depth are extracted from both the present and previously encoded coding units. The feature vectors are then used for generating a dimensionality reduction model and a classification model. The generated models at each coding depth are then used to predict the split decisions of subsequent coding units. Stepwise regression, random forest reduction and principal component analysis are used for dimensionality reduction; whereas, polynomial networks and random forests are utilized for classification. The proposed solution is assessed in terms of classification accuracy, BD-rate, BD-PSNR and computational time complexity. Using seventeen video sequences with four different classes of resolution, an average classification accuracy of 86.5% is reported for the proposed classification system. In comparison to regular HEVC coding, the proposed solution resulted in a BD-rate loss of 0.55 and a BD-PSNR of -0.02 dB. The average reported computational complexity reduction is found to be 39.2%.
    DSpace URI
    http://hdl.handle.net/11073/16373
    External URI
    https://doi.org/10.1007/s11042-018-6882-8
    Collections
    • Department of Computer Science and Engineering

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsCollege/DeptArchive ReferenceSeriesThis CollectionBy Issue DateAuthorsTitlesSubjectsCollege/DeptArchive ReferenceSeries

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    DSpace software copyright © 2002-2016  DuraSpace
    Submission Policies | Terms of Use | Takedown Policy | Privacy Policy | About Us | Contact Us | Send Feedback

    Return to AUS
    Theme by 
    Atmire NV