Unifying Pixel-Level Fidelity and Perceptual Realism in Image Distortion Measurement

Conceitos Básicos

Wasserstein distortion is a generalized distortion measure that simultaneously captures pixel-level fidelity and perceptual realism, unifying prior work on texture generation, image realism, and models of the early human visual system.

Resumo

The paper introduces Wasserstein distortion, a new distortion measure for images that simultaneously generalizes pixel-level fidelity and perceptual realism.

Key highlights:

• Wasserstein distortion is based on models of the human visual system, specifically the concept of receptive fields in the ventral stream that grow in size with eccentricity.
• It computes the distribution of local features at each location in the image using a pooling function, and measures the Wasserstein distance between these distributions for the reference and reconstructed images.
• When the pooling function is tightly localized, Wasserstein distortion reduces to a fidelity measure like MSE. When the pooling function is broad, it becomes a realism measure.
• The authors prove that Wasserstein distortion is a proper metric under certain conditions on the pooling function.
• Experimental results demonstrate the ability of Wasserstein distortion to generate images that smoothly transition from high fidelity to high realism, as well as to reproduce natural images while preserving perceptual quality in salient regions.

The paper unifies prior work on texture generation, image realism, and models of early visual processing into a single, optimizable distortion measure.

Estatísticas

The paper does not contain any explicit numerical data or statistics to support the key logics. The focus is on the theoretical development of the Wasserstein distortion measure and its experimental validation through image generation tasks.

Citações

"Wasserstein distortion attempts to generalize and unify prior work on texture generation, image realism and distortion, and models of the early human visual system, in the form of an optimizable metric in the mathematical sense."
"Wasserstein distortion reduces to a pure fidelity constraint or a pure realism constraint under different parameter choices and discuss its metric properties."
"Pairs of images that are close under Wasserstein distortion illustrate its utility."

Principais Insights Extraídos De

Wasserstein Distortion

by Yang... às arxiv.org 04-01-2024

https://arxiv.org/pdf/2310.03629.pdf

Perguntas Mais Profundas

How can Wasserstein distortion be extended to video and other spatio-temporal data?

Wasserstein distortion can be extended to video and other spatio-temporal data by considering the temporal dimension in addition to the spatial dimension. In the context of video, each frame can be treated as an individual image, and Wasserstein distortion can be calculated between corresponding frames in different videos. By incorporating the concept of pooling regions that vary over time, similar to how it varies spatially, Wasserstein distortion can capture the differences and similarities between videos in a spatio-temporal manner. This extension would involve computing the Wasserstein distance between distributions of features not only spatially but also temporally, allowing for a comprehensive comparison of videos based on both spatial and temporal characteristics.

What are the limitations of the current Wasserstein distortion formulation, and how could it be further improved to better capture human perception?

One limitation of the current Wasserstein distortion formulation is its sensitivity to the choice of pooling probability mass functions (PMFs). For instance, using a uniform PMF can lead to distortions being zero for certain pairs of images, even when they are visually distinct. To improve Wasserstein distortion for better capturing human perception, more robust and well-conditioned PMFs could be explored. Additionally, incorporating more advanced feature extraction methods, such as deep neural networks or steerable pyramids, could enhance the representation of images in the feature space. Furthermore, considering the dynamic nature of human perception, adapting the pooling regions based on saliency maps or other attention mechanisms could make Wasserstein distortion more aligned with how humans perceive visual stimuli.

What are the potential applications of Wasserstein distortion beyond image generation, such as in image compression, enhancement, or other processing tasks?

Beyond image generation, Wasserstein distortion has various potential applications in image processing tasks such as compression, enhancement, and analysis. In image compression, Wasserstein distortion can be used as a fidelity metric to optimize compression algorithms for preserving perceptual quality while reducing file size. By incorporating Wasserstein distortion into the compression process, the trade-off between compression efficiency and image fidelity can be better balanced. In image enhancement, Wasserstein distortion can guide algorithms to generate visually pleasing results by minimizing the discrepancy between the enhanced image and the original. Additionally, Wasserstein distortion can be utilized in tasks like style transfer, image restoration, and anomaly detection, where capturing perceptual differences is crucial for achieving high-quality results. Its ability to unify fidelity and realism makes Wasserstein distortion a versatile tool for a wide range of image processing applications.