# Full-Reference Calibration-Free Image Quality Assessment

arXiv: https://arxiv.org/abs/2205.12129 · HTML: https://mlsanity.com/paper/2205.12129/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 5 of 5)

- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration](https://mlsanity.com/paper/2007.12142/) (2020)
- [A Detail Based Method for Linear Full Reference Image Quality Prediction](https://mlsanity.com/paper/1709.03124/) (2017)

## Built on by (most cited first, 2 of 2)

- [Image Quality Assessment: Enhancing Perceptual Exploration and Interpretation with Collaborative Feature Refinement and Hausdorff distance](https://mlsanity.com/paper/2412.15847/) (2024)
- [BELE: Blur Equivalent Linearized Estimator](https://mlsanity.com/paper/2503.00503/) (2025)

## Abstract (arXiv)

One major problem of objective Image Quality Assessment (IQA) methods is the lack of linearity of their quality estimates with respect to scores expressed by human subjects. For this reason, usually IQA metrics undergo a calibration process based on subjective quality examples. However, example-based training makes generalization problematic, hampering result comparison across different applications and operative conditions. In this paper, new Full Reference (FR) techniques, providing estimates linearly correlated with human scores without using calibration are introduced. To reach this objective, these techniques are deeply rooted on principles and theoretical constraints. Restricting the interest on the IQA of the set of natural images, it is first recognized that application of estimation theory and psycho physical principles to images degraded by Gaussian blur leads to a so-called canonical IQA method, whose estimates are not only highly linearly correlated to subjective scores, but are also straightforwardly related to the Viewing Distance (VD). Then, it is shown that mainstream IQA methods can be reconducted to the canonical method applying a preliminary metric conversion based on a unique specimen image. The application of this scheme is then extended to a significant class of degraded images other than Gaussian blur, including noisy and compressed images. The resulting calibration-free FR IQA methods are suited for applications where comparability and interoperability across different imaging systems and on different VDs is a major requirement. A comparison of their statistical performance with respect to some conventional calibration prone methods is finally provided.