Understanding video texture — A basis for video compression

Angeliki Katsenou, Thomas Ntasios, Mariana Afonso, Dimitris Agrafiotis, David Bull

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

5 Citations (Scopus)
242 Downloads (Pure)


Encoding spatio-temporally varying textures is challenging for standardised video encoders, with significantly more bits required for textured blocks compared to non-textured blocks. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimize coding modes and performance. To this end, we examine the classification of video texture based on encoder performance. For this purpose, we employ spatio-temporal features and follow a two-step feature selection process by employing unsupervised machine learning approaches across the selected feature space. Finally, supervised machine learning approaches are applied on the set of the selected features that support classification prior to encoding with up to 95.1% accuracy. The results of this study offer the potential to underpin a new informed approach to a new informed approach to codec configuration and mode selection.
Original languageEnglish
Title of host publication2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP 2017)
Subtitle of host publicationProceedings of a meeting held 16-18 October 2017, Luton, United Kingdom
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)9781509036493
ISBN (Print)9781509036509
Publication statusPublished - Jan 2018

Publication series

ISSN (Print)2473-3628

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