NEURAL FIELD REAL-TIME TRANSMISSION USING MULTIPLE DESCRIPTION CODING WITH RANDOM POSITION SAMPLING

Neural fields are a new signal representation and are widely used now to represent various forms of multimedia. However, due to their size, they could introduce latency when delivered over the network. A way to mitigate that is to use Multiple description coding (MDC), that provides multiple descriptions of the same content which can then be transmitted along different paths to improve reliability/efficiency. In this paper, we introduce a novel MDC framework that is based on neural fields. We first apply a randomly sampled neural field (with small model size) to generate multiple descriptions based on random initializations. We leverage the fact that neural fields are continuous functions (constructed using a multi-layer perceptron (MLP)) and use that to reconstruct the entire original content and progressively improve the quality as more descriptions are obtained. We validate the effectiveness of the proposed method by showing results on public data sets.