Predicting video rate-distortion curves using textural features

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

5 Citations (Scopus)
282 Downloads (Pure)

Abstract

This work addresses the problem of predicting the compression efficiency of a video codec solely from features extracted from uncompressed content. Towards this goal, we have used a database of videos of homogeneous texture and extracted both spatial and frequency domain features. The videos are encoded using High Efficiency Video Coding (HEVC) reference codec at different quantization scales and their Rate-Distortion (RD) curves are modelled using linear regression. Using the extracted features and the fitted parameters of the RD model, a Support Vector Regression Model (SVRM) is trained to learn the relationship of the textural features with the RD curves. The SVRM is tested using iterative five-fold cross-validation. The presented experimental results demonstrate that RD curve characteristics can be predicted based on the textural features of the uncompressed videos, which offers potential benefits for encoder optimization.
Original languageEnglish
Title of host publicationPicture Coding Symposium (PCS), 2016
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781509059669
ISBN (Print)9781509059676
DOIs
Publication statusPublished - 24 Apr 2017

Keywords

  • Feature extraction
  • Dynamics
  • Correlation
  • Manganese
  • Bit rate
  • Enthropy
  • Video sequences

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  • Projects

    Vision for the Future-Full

    Bull, D. R.

    1/02/1531/01/20

    Project: Research

    Student Theses

    Intelligent Resampling Methods for Video Compression

    Author: Fernandez Afonso, M., 25 Jun 2019

    Supervisor: Agrafiotis, D. (Supervisor) & Bull, D. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)

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    Cite this

    Katsenou, A., Afonso, M., Agrafiotis, D., & Bull, D. (2017). Predicting video rate-distortion curves using textural features. In Picture Coding Symposium (PCS), 2016 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/PCS.2016.7906313