The paper studies the rate-distortion-perception (RDP) tradeoff for a memoryless source model in the asymptotic limit of large block-lengths. The perception measure is based on the divergence between the distributions of the source and reconstruction sequences conditioned on the encoder output, a novel approach introduced in previous works. The authors consider the case without shared randomness between the encoder and decoder. For discrete memoryless sources, they derive a single-letter characterization of the RDP function, addressing an open problem for the marginal metric. For continuous-valued sources under squared error distortion and squared quadratic Wasserstein perception measures, they also derive a single-letter characterization and show that adding noise at the decoder is sufficient to achieve the optimal representation. For zero perception loss, the characterization coincides with results for the marginal metric, demonstrating that zero perception loss can be achieved with a 3-dB penalty in the minimum distortion. The paper extends these results to Gaussian sources, deriving the RDP function for vector Gaussian sources and proposing a waterfilling solution.The paper studies the rate-distortion-perception (RDP) tradeoff for a memoryless source model in the asymptotic limit of large block-lengths. The perception measure is based on the divergence between the distributions of the source and reconstruction sequences conditioned on the encoder output, a novel approach introduced in previous works. The authors consider the case without shared randomness between the encoder and decoder. For discrete memoryless sources, they derive a single-letter characterization of the RDP function, addressing an open problem for the marginal metric. For continuous-valued sources under squared error distortion and squared quadratic Wasserstein perception measures, they also derive a single-letter characterization and show that adding noise at the decoder is sufficient to achieve the optimal representation. For zero perception loss, the characterization coincides with results for the marginal metric, demonstrating that zero perception loss can be achieved with a 3-dB penalty in the minimum distortion. The paper extends these results to Gaussian sources, deriving the RDP function for vector Gaussian sources and proposing a waterfilling solution.