Perceptually Coded Transmission of Arbitrary 3D Objects over Burst Packet Loss Channels Enhanced with a Generic JND Formulation

In this work we propose a new approach to account for burst packet loss during transmission of 3D objects represented by texture and mesh over unreliable networks. Our strategy includes applying stripification on the 3D mesh following the valence-driven algorithm and distributing nearby vertices into different packets, combined with an interleaving technique that does not need texture or mesh packets to be re-transmitted. The perceptually-driven technique is able to successfully interpolate lost mesh features even under severe packet loss. The reconstructed mesh is further improved by applying our curvature-driven probabilistic strategy to safeguard visually significant structures on the 3D surface. Experimental results show that smoothness on the object surface is preserved even at 50% packet loss. At 75% packet loss, smoothness on the object surface deteriorates but the overall shape of an object is still preserved. We also define a Quality of Experience (QoE) metric to formulate the Just-Noticeable-Difference (JND) concept, to quantify the qualitative findings obtained from earlier subjective user studies, which provides flexibility to applications for reducing the transmission of visually redundant data.