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

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.