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arXiv Computer Vision By Sheng Zhao, Weikai Lin, Yuhao Zhu

Multidimensional Observer Model and Perceptual Dimensions of Human Image Quality Assessment

Read the original on arXiv Computer Vision →

The paper introduces a multidimensional observer model that represents images as distributions in a latent perceptual space and models human image quality judgment as comparisons of noisy samples. By aligning the model with neural representations in the primate ventral stream and fitting it to large-scale behavioral data, the authors demonstrate that the perceptual space required for human quality assessment is extremely low-dimensional relative to the image space. The study reveals that the structure of this perceptual space differs between low-level and high-level quality judgments, indicating that humans construct task-dependent perceptual spaces during visual decision making.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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