Rate-Distortion-Perception Tradeoff for Gaussian Vector Sources
This paper studies the rate-distortion-perception (RDP) tradeoff for a Gaussian vector source coding problem where the goal is to compress the multi-component source subject to distortion and perception constraints. Specifically, the RDP setting with either the Kullback-Leibler (KL) divergence or Wasserstein-2 metric as the perception loss function is examined, and it is shown that for Gaussian vector sources, jointly Gaussian reconstructions are optimal.
ISIT Awards Session Brochures
At each ISIT, the leadership of the Â鶹´«Ã½AV Information Theory Society presents a number of awards, including the Thomas M. Cover Dissertation Award, the James L.