# Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

arXiv: https://arxiv.org/abs/1904.08632 · HTML: https://mlsanity.com/paper/1904.08632/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 21 of 21)

- [LMM-PCQA: Assisting Point Cloud Quality Assessment with LMM](https://mlsanity.com/paper/2404.18203/) (2024)
- [Quality-aware Pre-trained Models for Blind Image Quality Assessment](https://mlsanity.com/paper/2303.00521/) (2023)
- [UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment](https://mlsanity.com/paper/2406.01069/) (2024)
- [UIF: An Objective Quality Assessment for Underwater Image Enhancement](https://mlsanity.com/paper/2205.09392/) (2022)
- [CoA: Towards Real Image Dehazing via Compression-and-Adaptation](https://mlsanity.com/paper/2504.05590/) (2025)
- [Bilevel Layer-Positioning LoRA for Real Image Dehazing](https://mlsanity.com/paper/2603.10872/) (2026)
- [Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement](https://mlsanity.com/paper/2102.00670/) (2021)
- [Gamma: Toward Generic Image Assessment with Mixture of Assessment Experts](https://mlsanity.com/paper/2503.06678/) (2025)
- [Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network](https://mlsanity.com/paper/2308.03200/) (2023)
- [Perceptual Multi-Exposure Fusion](https://mlsanity.com/paper/2210.09604/) (2022)
- [Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter](https://mlsanity.com/paper/2306.09321/) (2023)
- [NDELS: A Novel Approach for Nighttime Dehazing, Low-Light Enhancement, and Light Suppression](https://mlsanity.com/paper/2312.06850/) (2023)
- [Quality-guided Skin Tone Enhancement for Portrait Photography](https://mlsanity.com/paper/2406.15848/) (2024)
- [Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition](https://mlsanity.com/paper/2311.02995/) (2023)
- [Deep Decomposition and Bilinear Pooling Network for Blind Night-Time Image Quality Evaluation](https://mlsanity.com/paper/2205.05880/) (2022)
- [A New Image Quality Database for Multiple Industrial Processes](https://mlsanity.com/paper/2401.13956/) (2024)
- [PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment](https://mlsanity.com/paper/2412.15527/) (2024)
- [No-Reference Image Contrast Assessment with Customized EfficientNet-B0](https://mlsanity.com/paper/2509.21967/) (2025)
- [No-reference Quality Assessment of Contrast-distorted Images using Contrast-enhanced Pseudo Reference](https://mlsanity.com/paper/2510.05053/) (2025)
- [Preference-Guided Debiasing for No-Reference Enhancement Image Quality Assessment](https://mlsanity.com/paper/2603.20086/) (2026)
- [Fine-Grained Anomaly Perception in Wild UGC-Enhanced Images: A Comprehensive Dataset and Difference-Fusion Framework](https://mlsanity.com/paper/2609.02529/) (2026)

## Abstract (arXiv)

In this paper we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g. object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g. visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images which are generally thought to be of the best quality. In this work, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measre of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image datasets. Results of experiments on nine datasets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-, reduced- and no-reference IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications.