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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. 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.