# MCIQA-2K: A Multi-Dimensional Dataset and No-Reference Quality Assessment Benchmark for Colorized Images

arXiv: https://arxiv.org/abs/2609.14495 · HTML: https://mlsanity.com/paper/2609.14495/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 13 Sep 2026.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment](https://mlsanity.com/paper/2308.03060/) (2023)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2306.04717/) (2023)
- [Instance-aware Image Colorization](https://mlsanity.com/paper/2005.10825/) (2020)
- [Towards Vivid and Diverse Image Colorization with Generative Color Prior](https://mlsanity.com/paper/2108.08826/) (2021)
- [A Perceptual Quality Assessment Exploration for AIGC Images](https://mlsanity.com/paper/2303.12618/) (2023)
- [DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders](https://mlsanity.com/paper/2212.11613/) (2022)
- [BigColor: Colorization using a Generative Color Prior for Natural Images](https://mlsanity.com/paper/2207.09685/) (2022)
- [Control Color: Multimodal Diffusion-based Interactive Image Colorization](https://mlsanity.com/paper/2402.10855/) (2024)
- [Quality-Aware Image-Text Alignment for Opinion-Unaware Image Quality Assessment](https://mlsanity.com/paper/2403.11176/) (2024)
- [Diffusing Colors: Image Colorization with Text Guided Diffusion](https://mlsanity.com/paper/2312.04145/) (2023)
- [Image Colorization: A Survey and Dataset](https://mlsanity.com/paper/2008.10774/) (2020)
- [BIQ2021: A Large-Scale Blind Image Quality Assessment Database](https://mlsanity.com/paper/2202.03879/) (2022)
- [MultiColor: Image Colorization by Learning from Multiple Color Spaces](https://mlsanity.com/paper/2408.04172/) (2024)
- [Language-based Image Colorization: A Benchmark and Beyond](https://mlsanity.com/paper/2503.14974/) (2025)

## Abstract (arXiv)

Image colorization is an inherently ill-posed task, since a single grayscale image may correspond to multiple plausible colorized results. Consequently, conventional full-reference image quality assessment (IQA) metrics fail to accurately reflect human perceptual preferences for colorized images. In this paper, we present MCIQA-2K, a large-scale multi-dimensional benchmark specifically designed for no-reference quality assessment of colorized images. We construct a dataset containing 2,000 colorized images generated by five representative colorization models, together with human annotations across three perceptual dimensions: color smearing, semantic color misalignment, and global naturalness. Building upon the proposed benchmark, we further introduce MCIQA, a dedicated multi-branch NR-IQA framework for colorized images. Extensive experiments demonstrate that MCIQA significantly outperforms existing full-reference and no-reference IQA methods on the proposed benchmark, while also exhibiting competitive generalization capability on several widely-used IQA datasets. The dataset and code are publicly available at https://github.com/ARBEZ-ZEBRA/MCIQA.