# Applicability limitations of differentiable full-reference image-quality

arXiv: https://arxiv.org/abs/2212.05499 · HTML: https://mlsanity.com/paper/2212.05499/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 11 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [PieAPP: Perceptual Image-Error Assessment through Pairwise Preference](https://mlsanity.com/paper/1806.02067/) (2018)
- [On the Suitability of Lp-norms for Creating and Preventing Adversarial Examples](https://mlsanity.com/paper/1802.09653/) (2018)
- [A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment](https://mlsanity.com/paper/1607.06140/) (2016)
- [E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles](https://mlsanity.com/paper/1906.03973/) (2019)
- [Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop](https://mlsanity.com/paper/2210.00933/) (2022)
- [Video compression dataset and benchmark of learning-based video-quality metrics](https://mlsanity.com/paper/2211.12109/) (2022)
- [ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression](https://mlsanity.com/paper/1910.08845/) (2019)
- [Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metrics](https://mlsanity.com/paper/2211.00366/) (2022)
- [Hacking VMAF and VMAF NEG: vulnerability to different preprocessing methods](https://mlsanity.com/paper/2107.04510/) (2021)
- [Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQE](https://mlsanity.com/paper/1907.03842/) (2019)

## Built on by (most cited first, 1 of 1)

- [Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment](https://mlsanity.com/paper/2411.12575/) (2024)

## Abstract (arXiv)

Subjective image-quality measurement plays a critical role in the development of image-processing applications. The purpose of a visual-quality metric is to approximate the results of subjective assessment. In this regard, more and more metrics are under development, but little research has considered their limitations. This paper addresses that deficiency: we show how image preprocessing before compression can artificially increase the quality scores provided by the popular metrics DISTS, LPIPS, HaarPSI, and VIF as well as how these scores are inconsistent with subjective-quality scores. We propose a series of neural-network preprocessing models that increase DISTS by up to 34.5%, LPIPS by up to 36.8%, VIF by up to 98.0%, and HaarPSI by up to 22.6% in the case of JPEG-compressed images. A subjective comparison of preprocessed images showed that for most of the metrics we examined, visual quality drops or stays unchanged, limiting the applicability of these metrics.