Rate-Distortion Optimized Interactive Light Field Streaming

Abstract

High-quality, photorealistic image-based rendering datasets are typically too large to download entirely before viewing, even when compressed. It is more suitable to instead stream the required image data to a remote user who can start interacting with the dataset immediately. This paper presents an interactive light field streaming system and proposes packet scheduling for transmitting the encoded image data in a rate-distortion optimized manner. An interactive light field streaming system must have low user latency. The system presented in this paper predicts the future user viewing trajectory to mitigate the effects of the low-latency constraints. Experimental results show that view prediction can improve performance, and that this improvement is limited by the prediction accuracy. The proposed packet scheduling algorithm considers network conditions and rate-distortion cost, knowledge of sent and received images, and the distortion for a set of images, to optimize the rendered image quality for the remote user. Rate-distortion optimized scheduling can be implemented either at the receiver or the sender. It is shown that this rate-distortion optimized packet scheduling can significantly improve performance over a heuristic scheduling approach. Experimental results also show that the encoding prediction dependency structure affects streaming performance both through the compression efficiency of the encoding and also through any decoding dependencies that may be introduced