基于双阶颜色信息的色域映射图像无参考质量评价算法

何秋宏; 余伟; 郭志林; 袁连海; 刘玉英

基于双阶颜色信息的色域映射图像无参考质量评价算法

1.
成都理工大学工程技术学院电子信息与计算机工程系, 四川乐山 614000
2.
中国矿业大学信息与控制工程学院, 江苏徐州 221116

基金项目:

国家自然科学基金（青年项目） 62101084

四川省大学生创新创业训练项目 s202313668011

乐山市科技局重点研究项目 A322021010

成都理工大学工程技术学院院级基金 C12202312

详细信息

作者简介:
何秋宏（2002-），男，学生，本科，主要研究方向：图像质量评价。E-mail：2398941913@qq.com

通讯作者:
余伟（1993-），男，讲师，硕士，中国计算机学会会员，主要研究方向：图像处理、深度学习。E-mail：yuweits@sicnu.edu.cn

中图分类号: TP391.41
计量
- 文章访问数: 31
- HTML全文浏览量: 3
- PDF下载量: 8
出版历程
- 收稿日期: 2024-01-19
- 修回日期: 2024-02-28
- 刊出日期: 2025-03-19

No-reference Quality Evaluation Algorithm for Color Gamut Mapped Images Based on Double-Order Color Information

1.
Dept. of Electronic Information & Computer Engineering, the Engineering & Technical College of Chengdu University of Technology, Leshan 614000, China
2.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

摘要

摘要:
色域映射是用于在不同设备之间实现彩色图像高保真传输的一种技术。但通过色域映射得到的图像不可避免因颜色信息的损失产生严重的伪影和失真，从而导致纹理结构失真和色彩自然度失真。基于颜色信息损失严重的事实，本文提出了一种基于双阶颜色表示的无参考质量评价方法。传统图像质量评价方法大多基于灰度域提取质量感知特征，少数考虑颜色信息的方法也仅从色调、饱和度等颜色分量中提取特征。色调、饱和度均是通过R、G、B三个颜色分量线性计算而得，忽略了颜色的导数信息。因此本文算法从零阶颜色信息（R、G、B颜色分量）和一阶颜色信息（即导数信息）中进行特征提取，并将所提特征进行回归训练得到质量预测模型。实验证明，该模型对色域映射图像质量的预测性能优于现有的无参考质量评价方法。
- 色域映射 /
- 图像质量评价 /
- 颜色信息 /
- 特征提取 /
- 回归训练
Abstract:
Gamut mapping is a technology used to achieve high-fidelity transmission of color images between different devices. However, an image obtained through gamut mapping inevitably produces serious artifacts and distortions because of color information loss, which leads to distortions in texture and color naturalness. Since color information loss is serious in gamut mapped images (GMIs), a no-reference quality evaluation method based on double-order color representation is proposed. Many traditional image quality assessment (IQA) methods extract quality-aware features (QAFs) in the gray domain, and a few IQA methods extract QAFs from color components, such as hue and saturation. Hue and saturation were calculated linearly using the R, G, and B color components while ignoring the derivative information of the color. Therefore, this study extracted features from zero-order (R, G, and B components) and first-order (derivative) color information. These features were then used for regression training to obtain a quality prediction model. Experimental results show that the model is superior to existing no-reference quality evaluation methods in predicting the quality of GMIs.
- gamut mapping /
- image quality assessment /
- color information /
- feature extraction /
- regression training

HTML全文

图 1 原始高清图像和3张由不同色域映射算法生成的GMIs

Figure 1. The original image and three GMIs generated by different gamut mapping algorithms

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图 2 原始高清图像和色域映射图像在零阶和一阶的颜色分量图。(a) 原始图像；(b)-(f)分别为(a)的OA、SA、R、G、B分量图；(g) 色域映射图像；(h)-(l)分别为(g)的OA、SA、R、G、B分量图

Figure 2. Zero-order and first-order color component of original image and GMI. (a) is the original image; (b)-(f) are OA、SA、R、G、B component of (a) respectively; (g) is the GMI; (h)-(l) are OA、SA、R、G、B component of (g) respectively

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图 3 双阶颜色信息质量评价算法框架

Figure 3. Flowchart of the double-order color information quality evaluation

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图 4 图 1中4幅图像一阶颜色分量的相邻差值经验分布图系数分布

Figure 4. Empirical distribution of adjacent difference of first-order color components of the four images in Figure 1

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图 5 图 1中4张图的MSCN系数统计

Figure 5. Statistical chart of MSCN coefficients for 4 images in Fig. 1

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图 6 两阶颜色信息对算法性能的贡献

Figure 6. Contribution of two-order color information to algorithm performance

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图 7 R、G、B三个零阶颜色分量的性能贡献

Figure 7. Performance contribution of R, G and B zero-order color components

下载: 全尺寸图片幻灯片

表 1 三个数据库中算法性能比较

Table 1 Comparative evaluation on the three gamut mapping databases

Method	BS database			IG database			LC database
Method	PLCC	SRCC	KRCC	PLCC	SRCC	KRCC	PLCC	SRCC	KRCC
BRISQUE	0.7633	0.5678	0.4126	0.5153	0.4654	0.3345	0.5026	0.5274	0.3802
DESIQUE	0.8213	0.5941	0.4354	0.5987	0.5666	0.4211	0.5692	0.5973	0.4429
NFERM	0.7441	0.5566	0.4072	0.4399	0.4510	0.2968	0.4934	0.4985	0.3617
IDEAL	0.7859	0.6652	0.4994	0.6195	0.6139	0.4550	0.5780	0.5989	0.4417
GMNSS	0.8170	0.6774	0.5100	0.7369	0.7086	0.5526	0.6256	0.6154	0.4630
GMCSD	0.8374	0.7028	0.5402	0.7508	0.7273	0.5633	0.6778	0.6848	0.5152
BLGS	0.7865	0.7275	0.5116	0.7464	0.7165	0.5147	0.7573	0.7074	0.5033
GMLCH	0.8385	0.7069	0.5337	0.6302	0.6039	0.4523	0.6565	0.6498	0.4876
BOSS	0.4830	0.4826	0.3531	0.4573	0.4318	0.3167	0.7035	0.6021	0.4490
VQGC	0.8315	0.7016	0.5334	0.5732	0.5442	0.4057	0.6298	0.6332	0.4675
Proposed	0.8816	0.7394	0.5735	0.7944	0.7586	0.5929	0.7252	0.7360	0.5655

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表 2 NSS特征与非NSS统计特征的性能对比

Table 2 Performance comparison of NSS features with NON-NSS statistical features

	BS			IG			LC
	PLCC	SRCC	KRCC	PCC	SRCC	KRCC	PCC	SRCC	KRCC
NSS	0.8098	0.6799	0.5171	0.5891	0.5721	0.4175	0.6550	0.6634	0.4928
NO NSS	0.7038	0.4725	0.3394	0.5645	0.5241	0.3912	0.4713	0.3620	0.2677
Proposed	0.8816	0.7394	0.5735	0.7944	0.7586	0.5929	0.7252	0.7360	0.5655

下载: 导出CSV

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