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Found 7 papers, 1 papers with code
The Rate-Distortion-Perception Trade-off: The Role of Private Randomness
The per-symbol near-perfect realism constraint requires that the TVD between the distribution of output symbol $Y_t$ and the source distribution be arbitrarily small, uniformly in the index $t.$ We characterize the corresponding asymptotic rate-distortion trade-off and show that encoder private randomness is not useful if the compression rate is lower than the entropy of the source, however limited the resources in terms of common randomness and decoder private randomness may be.
Wasserstein Distortion: Unifying Fidelity and Realism
We introduce a distortion measure for images, Wasserstein distortion, that simultaneously generalizes pixel-level fidelity on the one hand and realism or perceptual quality on the other.
Do Neural Networks Compress Manifolds Optimally?
Artificial Neural-Network-based (ANN-based) lossy compressors have recently obtained striking results on several sources.
On One-Bit Quantization
We consider the one-bit quantizer that minimizes the mean squared error for a source living in a real Hilbert space.
The Rate-Distortion-Perception Tradeoff: The Role of Common Randomness
A rate-distortion-perception (RDP) tradeoff has recently been proposed by Blau and Michaeli and also Matsumoto.
Principal Bit Analysis: Autoencoding with Schur-Concave Loss
As one application, we consider a strictly Schur-concave constraint that estimates the number of bits needed to represent the latent variables under fixed-rate encoding, a setup that we call \emph{Principal Bit Analysis (PBA)}.
A coding theorem for the rate-distortion-perception function
The rate-distortion-perception function (RDPF; Blau and Michaeli, 2019) has emerged as a useful tool for thinking about realism and distortion of reconstructions in lossy compression.
