# PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance

arXiv: https://arxiv.org/abs/1910.12548 · HTML: https://mlsanity.com/paper/1910.12548/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception](https://mlsanity.com/paper/2102.10951/) (2021)
- [Assessing invariance to affine transformations in image quality metrics](https://mlsanity.com/paper/2407.17927/) (2024)
- [Image Statistics Predict the Sensitivity of Perceptual Quality Metrics](https://mlsanity.com/paper/2303.09874/) (2023)
- [RAID-Database: human Responses to Affine Image Distortions](https://mlsanity.com/paper/2412.10211/) (2024)
- [Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset](https://mlsanity.com/paper/2203.13558/) (2022)
- [Analysis of Deep Image Quality Models](https://mlsanity.com/paper/2302.13345/) (2023)
- [Evaluating Perceptual Distance Models by Fitting Binomial Distributions to Two-Alternative Forced Choice Data](https://mlsanity.com/paper/2403.10390/) (2024)
- [Parametric PerceptNet: A bio-inspired deep-net trained for Image Quality Assessment](https://mlsanity.com/paper/2412.03210/) (2024)
- [From Images to Perception: Emergence of Perceptual Properties by Reconstructing Images](https://mlsanity.com/paper/2508.10450/) (2025)

## Abstract (arXiv)

Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was usually taken from the human visual perceptual system and how the system processes different perturbations in order to replicate to what extent it determines our ability to judge image quality. While recent works have presented deep neural networks trained to predict human perceptual quality, very few borrow any intuitions from the human visual system. To address this, we present PerceptNet, a convolutional neural network where the architecture has been chosen to reflect the structure and various stages in the human visual system. We evaluate PerceptNet on various traditional perception datasets and note strong performance on a number of them as compared with traditional image quality metrics. We also show that including a nonlinearity inspired by the human visual system in classical deep neural networks architectures can increase their ability to judge perceptual similarity. Compared to similar deep learning methods, the performance is similar, although our network has a number of parameters that is several orders of magnitude less.