# Comparison of Image Quality Models for Optimization of Image Processing Systems

arXiv: https://arxiv.org/abs/2005.01338 · HTML: https://mlsanity.com/paper/2005.01338/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 8 of 8)

- [Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis](https://mlsanity.com/paper/2106.11485/) (2021)
- [DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space](https://mlsanity.com/paper/2208.03323/) (2022)
- [Similarity and Quality Metrics for MR Image-To-Image Translation](https://mlsanity.com/paper/2405.08431/) (2024)
- [Machine Perception-Driven Image Compression: A Layered Generative Approach](https://mlsanity.com/paper/2304.06896/) (2023)
- [Five Pitfalls When Assessing Synthetic Medical Images with Reference Metrics](https://mlsanity.com/paper/2408.06075/) (2024)
- [Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views](https://mlsanity.com/paper/2101.10396/) (2021)
- [TreeNet: A Light Weight Model for Low Bitrate Image Compression](https://mlsanity.com/paper/2512.16743/) (2025)
- [TRIG: Toward Measuring Texture Realism in Image Generation](https://mlsanity.com/paper/2610.15164/) (2026)

## Abstract (arXiv)

The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments. Perceptual datasets gathered for this purpose have provided useful benchmarks for improving IQA methods, but their heavy use creates a risk of overfitting. Here, we perform a large-scale comparison of IQA models in terms of their use as objectives for the optimization of image processing algorithms. Specifically, we use eleven full-reference IQA models to train deep neural networks for four low-level vision tasks: denoising, deblurring, super-resolution, and compression. Subjective testing on the optimized images allows us to rank the competing models in terms of their perceptual performance, elucidate their relative advantages and disadvantages in these tasks, and propose a set of desirable properties for incorporation into future IQA models.