# Improving Maximum Likelihood Difference Scaling method to measure inter content scale

arXiv: https://arxiv.org/abs/2203.13186 · HTML: https://mlsanity.com/paper/2203.13186/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization](https://mlsanity.com/paper/2004.05691/) (2020)
- [Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation](https://mlsanity.com/paper/1810.08851/) (2018)
- [Subjective Image Quality Assessment with Boosted Triplet Comparisons](https://mlsanity.com/paper/2108.00201/) (2021)
- [Scalable Bayesian Preference Learning for Crowds](https://mlsanity.com/paper/1912.01987/) (2019)
- [Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy](https://mlsanity.com/paper/2010.00370/) (2020)
- [HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation](https://mlsanity.com/paper/1711.05957/) (2017)

## Abstract (arXiv)

The goal of most subjective studies is to place a set of stimuli on a perceptual scale. This is mostly done directly by rating, e.g. using single or double stimulus methodologies, or indirectly by ranking or pairwise comparison. All these methods estimate the perceptual magnitudes of the stimuli on a scale. However, procedures such as Maximum Likelihood Difference Scaling (MLDS) have shown that considering perceptual distances can bring benefits in terms of discriminatory power, observers' cognitive load, and the number of trials required. One of the disadvantages of the MLDS method is that the perceptual scales obtained for stimuli created from different source content are generally not comparable. In this paper, we propose an extension of the MLDS method that ensures inter-content comparability of the results and shows its usefulness especially in the presence of observer errors.