# No-Reference Image Contrast Assessment with Customized EfficientNet-B0

arXiv: https://arxiv.org/abs/2509.21967 · HTML: https://mlsanity.com/paper/2509.21967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2004.05508/) (2020)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [On the Use of Deep Learning for Blind Image Quality Assessment](https://mlsanity.com/paper/1602.05531/) (2016)
- [DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning](https://mlsanity.com/paper/2001.08113/) (2020)
- [Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data](https://mlsanity.com/paper/1904.08632/) (2019)
- [No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement](https://mlsanity.com/paper/1904.08879/) (2019)
- [Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive Learning](https://mlsanity.com/paper/2312.06995/) (2023)
- [Deep Ensembling for Perceptual Image Quality Assessment](https://mlsanity.com/paper/2305.09141/) (2023)
- [(ASNA) An Attention-based Siamese-Difference Neural Network with Surrogate Ranking Loss function for Perceptual Image Quality Assessment](https://mlsanity.com/paper/2105.02531/) (2021)
- [Efficient No-Reference Quality Assessment and Classification Model for Contrast Distorted Images](https://mlsanity.com/paper/1804.02554/) (2018)
- [Image Quality Assessment with Gradient Siamese Network](https://mlsanity.com/paper/2208.04081/) (2022)

## Abstract (arXiv)

Image contrast was a fundamental factor in visual perception and played a vital role in overall image quality. However, most no reference image quality assessment NR IQA models struggled to accurately evaluate contrast distortions under diverse real world conditions. In this study, we proposed a deep learning based framework for blind contrast quality assessment by customizing and fine-tuning three pre trained architectures, EfficientNet B0, ResNet18, and MobileNetV2, for perceptual Mean Opinion Score, along with an additional model built on a Siamese network, which indicated a limited ability to capture perceptual contrast distortions. Each model is modified with a contrast-aware regression head and trained end to end using targeted data augmentations on two benchmark datasets, CID2013 and CCID2014, containing synthetic and authentic contrast distortions. Performance is evaluated using Pearson Linear Correlation Coefficient and Spearman Rank Order Correlation Coefficient, which assess the alignment between predicted and human rated scores. Among these three models, our customized EfficientNet B0 model achieved state-of-the-art performance with PLCC = 0.9286 and SRCC = 0.9178 on CCID2014 and PLCC = 0.9581 and SRCC = 0.9369 on CID2013, surpassing traditional methods and outperforming other deep baselines. These results highlighted the models robustness and effectiveness in capturing perceptual contrast distortion. Overall, the proposed method demonstrated that contrast aware adaptation of lightweight pre trained networks can yield a high performing, scalable solution for no reference contrast quality assessment suitable for real time and resource constrained applications.