Wasserstein Distortion: Unifying fidelity and realism

Yang Qiu, Aaron B. Wagner, Johannes Ballé, Lucas Theis

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: information theory, texture synthesis, distortion-realism tradeoff, distortion-perception tradeoff

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Abstract: 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. We show how Wasserstein distortion reduces mathematically to a pure fidelity constraint or a pure realism constraint under different parameter choices. Pairs of images that are close under Wasserstein distortion illustrate its utility. In particular, we generate random textures that have high fidelity to a reference texture in one location of the image and smoothly transition to an independent realization of the texture as one moves away from this point. Connections between Wasserstein distortion and models of the human visual system are noted.

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Submission Number: 2083