Problem
Research questions and friction points this paper is trying to address.
Image Colorization
No-Reference Quality Assessment
Perceptual Preferences
MCIQA-2K: A Multi-Dimensional Dataset and No-Reference Quality Assessment Benchmark for Colorized Images
🤖 AI Summary
本文针对彩色图像质量评估问题，构建了MCIQA-2K数据集，并提出一个多分支无参考质量评估框架MCIQA，以更准确地反映人类对彩色图像的感知偏好。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
no-reference quality assessment
multi-dimensional benchmark
colorized images
perceptual dimensions
MCIQA framework
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
