Video texture analysis based on HEVC encoding statistics

Mariana Afonso; Angeliki Katsenou; Aaron Zhang; Dimitris Agrafiotis; David Bull

doi:10.1109/PCS.2016.7906312

Video texture analysis based on HEVC encoding statistics

Mariana Afonso, Angeliki Katsenou, Aaron Zhang, Dimitris Agrafiotis, David Bull

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

9 Citations (Scopus)

511 Downloads (Pure)

Abstract

In this paper, an extensive study of different video texture properties based on encoding statistics extracted from the HEVC HM reference software is presented. Mode selection, partitioning, motion vectors and bitrate allocation are among the statistics obtained from the encoder. For this study, a new dataset
of homogeneous static and dynamic video textures, HomTex, is proposed. A comprehensive investigation of the results reveals a significant variability of coding statistics within dynamic textures, suggesting that this category should be further split into two relevant subcategories, continuous dynamic textures and discrete dynamic textures. This case is supported by an unsupervised
learning approach on the statistics extracted. Finally, following the results obtained, some suggestions of improvements in video texture coding are presented.

Original language	English
Title of host publication	Picture Coding Symposium (PCS), 2016
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	5
ISBN (Electronic)	9781509059669, 9781509059676
DOIs	https://doi.org/10.1109/PCS.2016.7906312
Publication status	Published - 24 Apr 2017

Keywords

Texture Classification
Video Coding
Encoding Statistics
Dynamic Textures
HEVC

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10.1109/PCS.2016.7906312

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Vision for the Future-Full
Bull, D. R. (Principal Investigator)
1/02/15 → 31/01/20
Project: Research

Intelligent Resampling Methods for Video Compression
Fernandez Afonso, M. (Author), Agrafiotis, D. (Supervisor) & Bull, D. (Supervisor), 25 Jun 2019
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)

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@inproceedings{23c1f19b247848bba1daef2b772ec22a,

title = "Video texture analysis based on HEVC encoding statistics",

abstract = "In this paper, an extensive study of different video texture properties based on encoding statistics extracted from the HEVC HM reference software is presented. Mode selection, partitioning, motion vectors and bitrate allocation are among the statistics obtained from the encoder. For this study, a new datasetof homogeneous static and dynamic video textures, HomTex, is proposed. A comprehensive investigation of the results reveals a significant variability of coding statistics within dynamic textures, suggesting that this category should be further split into two relevant subcategories, continuous dynamic textures and discrete dynamic textures. This case is supported by an unsupervisedlearning approach on the statistics extracted. Finally, following the results obtained, some suggestions of improvements in video texture coding are presented.",

keywords = "Texture Classification, Video Coding, Encoding Statistics, Dynamic Textures, HEVC",

author = "Mariana Afonso and Angeliki Katsenou and Aaron Zhang and Dimitris Agrafiotis and David Bull",

year = "2017",

month = apr,

day = "24",

doi = "10.1109/PCS.2016.7906312",

language = "English",

booktitle = "Picture Coding Symposium (PCS), 2016",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

}

TY - GEN

T1 - Video texture analysis based on HEVC encoding statistics

AU - Afonso, Mariana

AU - Katsenou, Angeliki

AU - Zhang, Aaron

AU - Agrafiotis, Dimitris

AU - Bull, David

PY - 2017/4/24

Y1 - 2017/4/24

N2 - In this paper, an extensive study of different video texture properties based on encoding statistics extracted from the HEVC HM reference software is presented. Mode selection, partitioning, motion vectors and bitrate allocation are among the statistics obtained from the encoder. For this study, a new datasetof homogeneous static and dynamic video textures, HomTex, is proposed. A comprehensive investigation of the results reveals a significant variability of coding statistics within dynamic textures, suggesting that this category should be further split into two relevant subcategories, continuous dynamic textures and discrete dynamic textures. This case is supported by an unsupervisedlearning approach on the statistics extracted. Finally, following the results obtained, some suggestions of improvements in video texture coding are presented.

AB - In this paper, an extensive study of different video texture properties based on encoding statistics extracted from the HEVC HM reference software is presented. Mode selection, partitioning, motion vectors and bitrate allocation are among the statistics obtained from the encoder. For this study, a new datasetof homogeneous static and dynamic video textures, HomTex, is proposed. A comprehensive investigation of the results reveals a significant variability of coding statistics within dynamic textures, suggesting that this category should be further split into two relevant subcategories, continuous dynamic textures and discrete dynamic textures. This case is supported by an unsupervisedlearning approach on the statistics extracted. Finally, following the results obtained, some suggestions of improvements in video texture coding are presented.

KW - Texture Classification

KW - Video Coding

KW - Encoding Statistics

KW - Dynamic Textures

KW - HEVC

U2 - 10.1109/PCS.2016.7906312

DO - 10.1109/PCS.2016.7906312

M3 - Conference Contribution (Conference Proceeding)

BT - Picture Coding Symposium (PCS), 2016

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Video texture analysis based on HEVC encoding statistics

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Keywords

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Vision for the Future-Full

Student theses

Intelligent Resampling Methods for Video Compression

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