On human Time-Varying Mesh compression exploiting activity-related characteristics

In this work, we explore the potential of exploiting activity-related global features in order to improve the performance of an existing human Time-Varying Mesh (TVM) compression scheme. The TVM compression scheme used, employs two kinds of frames, namely Intra(I)-Fames and Enhanced Predicted(EP) Frames. In this scheme, I-Frames are used as a reference to encode EP-Frames. The paper introduces a strategy for selecting the most appropriate I-Frame that will serve as a reference frame for the encoding of EP-Frames, exploiting activity-related characteristics. Two different strategies are presented, using a skeleton-matching criterion and a periodicity measurement metric based on human skeleton. Evaluation is conducted on two sequences of the MPEG-3DGC database [1]. Results show that the concept is sound, but they also reveal the sensitivity of the proposed methods to the skeleton quality, thus the need for more robust skeleton tracking techniques.