Study of Compression Statistics and Prediction of Rate-Distortion Curves for Video Texture

Angeliki V. Katsenou*, Mariana Afonso, David R. Bull

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

Encoding textural content remains a challenge for current standardised video codecs. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimise encoding performance. In this paper, we analyse the spatio-temporal features and statistics of video textures, explore the rate-quality performance of different texture types and investigate models to mathematically describe them. For all considered theoretical models, we employ machine-learning regression to predict the rate-quality curves based solely on selected spatio-temporal features extracted from uncompressed content. All experiments were performed on homogeneous video textures to ensure validity of the observations. The results of the regression indicate that using an exponential model we can more accurately predict the expected rate-quality curve (with a mean Bjøntegaard Delta rate of .46% over the considered dataset), while maintaining a low relative complexity. This is expected to be adopted by in the loop processes for faster encoding decisions such as rate–distortion optimisation, adaptive quantisation, partitioning, etc.

Original languageEnglish
Article number116551
Number of pages17
JournalSignal Processing: Image Communication
Volume101
Early online date17 Nov 2021
DOIs
Publication statusPublished - 1 Feb 2022

Bibliographical note

Funding Information:
All authors were with the Bristol Vision Institute and the Visual Information Lab, Department of Electrical and Electronic Engineering, University of Bristol, UK. The work presented was supported by the “Marie Skłodowska-Curie Actions- The EU Seventh Framework Programme for Research” project PROVISION ( 608231 ), the Engineering and Physical Sciences Research Council (EPSRC) , EP/M000885/1 , and the Leverhulme Early Career Fellowship ( ECF-2017-413 ).

Funding Information:
All authors were with the Bristol Vision Institute and the Visual Information Lab, Department of Electrical and Electronic Engineering, University of Bristol, UK. The work presented was supported by the ?Marie Sk?odowska-Curie Actions- The EU Seventh Framework Programme for Research? project PROVISION (608231), the Engineering and Physical Sciences Research Council (EPSRC), EP/M000885/1, and the Leverhulme Early Career Fellowship (ECF-2017-413).

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • Video texture
  • Video compression
  • Rate-distortion curves
  • HEVC

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