NDLI: Image based approach with k-mean clustering for the compression of human motion sequences

Content Provider	IEEE Xplore Digital Library
Author	Boon-Seng Chew Lap-Pui Chau Kim-Hui Yap
Copyright Year	2011
Description	Author affiliation: School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798 (Boon-Seng Chew; Lap-Pui Chau; Kim-Hui Yap)
Abstract	In this paper, an image format named Virtual Character Animation Image (VCAI) is presented for providing an efficient form of representation for humanoid motion data. By mapping the VCA motion information as 2-D images, characteristics of joint's correlation for the skeletal avatar and temporal coherence within the motion data are jointly reflected as spatial correlation of an image to aid compression. Since the VCA is now encoded as an image, the use of image processing tools and image delivery techniques are now possible. Lastly, a modified motion filter (MMF) is proposed to minimize the visual discontinuity in VCA's motion due to the quantization and transmission noise at high compression rate. The MMF helps to remove high frequency noise components and smoothen the motion signal providing perceptually improved VCA with reduction in distortion. Simulation results demonstrate the effectiveness of the proposed scheme ensuring the minor degradation of VCA quality measured by objective error metric and perceptual loss to the VCA for highly compressed motion stream.
Starting Page	1964
Ending Page	1967
File Size	259248
Page Count	4
File Format	PDF
ISBN	9781424494736
ISSN	02714302
e-ISBN	9781424494743
e-ISBN	9781424494729
DOI	10.1109/ISCAS.2011.5937975
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-15
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image coding Animation Joints Transform coding Three dimensional displays Solid modeling Coherence
Content Type	Text
Resource Type	Article

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