Using PFA in Feature Analysis and Selection for H.264 Adaptation
Authors: Nora A. Naguib, Ahmed E. Hussein, Hesham A. Keshk, Mohamed I. El-Adawy
Abstract:
Classification of video sequences based on their contents is a vital process for adaptation techniques. It helps decide which adaptation technique best fits the resource reduction requested by the client. In this paper we used the principal feature analysis algorithm to select a reduced subset of video features. The main idea is to select only one feature from each class based on the similarities between the features within that class. Our results showed that using this feature reduction technique the source video features can be completely omitted from future classification of video sequences.
Keywords: Adaptation, feature selection, H.264, Principal Feature Analysis (PFA)
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330055
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1610References:
[1] Vidyut Samanta, Ricardo Oliveira, Advait Dixit, Parixit Aghera, Petros Zerfos, Songwu Lu, "Impact of Video Encoding Parameters on Dynamic Video Transcoding", in IEEE COMSWARE, Delhi, India, January 2006.
[2] Yong Wang, Jae-Gon Kim, Shih-Fu Chang, Hyung-Myung Kim,"Utility- Based Video Adaptation for Universal Multimedia Access (UMA) and Content-Based Utility Function Prediction for Real-Time Video Transcoding", Multimedia, IEEE Transactions on, Volume 9, Issue 2, Feb. 2007
[3] Dimitrios Miras, "On Quality Aware Adaptation of Internet Video", University of London, PhD dissertation, May 2004
[4] Catalina Crespi de Arriba, "Subjective Video Quality Evaluation and Estimation for H.264 Codec and QVGA Resolution Sequences", PhD dissertation, 2007
[5] Test sequences: www.cipr.rpi.edu
[6] H.264 C++ implementation: http://iphome.hhi.de/suehring/tml/ Version: JM13
[7] ITU-T (Prepublished Recommendation) H.264 (05/2003): Advanced Video Coding for Generic Audiovisual Services
[8] Yijuan Lu, Ira Cohen, Xiang Sean Zhou, Qi Tian, "Feature selection using principal feature analysis", Proceedings of the 15th international conference on Multimedia, Augsburg, Germany, September 24-29, 2007. ACM 2007, ISBN 978-1-59593-702-5, Pages 301-304