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视觉流连通性可预测图像质量评估。

Visual stream connectivity predicts assessments of image quality.

机构信息

Brain Engineering Laboratory, Department of Psychological and Brain Sciences, Dartmouth, Hanover, NH, USA.

出版信息

J Vis. 2022 Oct 4;22(11):4. doi: 10.1167/jov.22.11.4.

Abstract

Despite extensive study of early vision, new and unexpected mechanisms continue to be identified. We introduce a novel formal treatment of the psychophysics of image similarity, derived directly from straightforward connectivity patterns in early visual pathways. The resulting differential geometry formulation is shown to provide accurate and explanatory accounts of human perceptual similarity judgments. The direct formal predictions are then shown to be further improved via simple regression on human behavioral reports, which in turn are used to construct more elaborate hypothesized neural connectivity patterns. It is shown that the predictive approaches introduced here outperform a standard successful published measure of perceived image fidelity; moreover, the approach provides clear explanatory principles of these similarity findings.

摘要

尽管早期视觉研究已经非常广泛,但新的、意想不到的机制仍在不断被发现。我们引入了一种新颖的图像相似性心理物理学的形式化处理方法,该方法直接源自早期视觉通路中的直接连通模式。所得到的微分几何公式被证明可以为人类感知相似性判断提供准确和有解释力的描述。然后,通过对人类行为报告的简单回归进一步改进直接形式预测,进而用于构建更精细的假设神经连通模式。结果表明,这里引入的预测方法优于标准的、成功的感知图像逼真度的已发表测量方法;此外,该方法还为这些相似性发现提供了明确的解释原则。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b52/9580224/2a57814331f0/jovi-22-11-4-f001.jpg

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