基于最小位移可视差的连续Seam Carving算法在图像缩放中的研究

崔嘉; 宋磊; 陆宏菊; 唐明晰; 戚萌

doi:10.11999/JEIT191050

基于最小位移可视差的连续Seam Carving算法在图像缩放中的研究

doi: 10.11999/JEIT191050

崔嘉^{1, 2},
宋磊^{1, 2},
陆宏菊³,
唐明晰⁴,
戚萌^{1, 2, ,}

1.
山东师范大学信息科学与工程学院济南 250358
2.
山东师范大学智能信息计算与安全实验室济南 250358
3.
济南技师学院济南 250031
4.
香港理工大学设计学院香港 999077

基金项目: 国家自然科学基金(61902225, 61502285)，山东省青年基金(ZR2014FQ013)

详细信息

作者简介:
崔嘉：男，1982年生，副教授，研究方向为图像处理、计算机辅助设计、智能设计

宋磊：男，1993年生，硕士生，研究方向为图像处理

陆宏菊：女，1982年生，讲师，研究方向为图像处理、语义分析

唐明晰：男，1956年生，教授，研究方向为智能设计、设计推理、计算机图形学

戚萌：女，1983年生，讲师，研究方向为虚拟现实、计算机图形学

通讯作者:
戚萌　qimeng@sdnu.edu.cn

中图分类号: TP391.41
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出版历程
- 收稿日期: 2019-12-30
- 修回日期: 2020-10-23
- 网络出版日期: 2020-12-11
- 刊出日期: 2021-04-20

Continuous Seam Carving Algorithm Based on Just Noticeable Distortion Algorithm in Image Retargeting

Jia CUI^{1, 2},
Lei SONG^{1, 2},
Hongju LU³,
Mingxi TANG⁴,
Meng QI^{1, 2
, ,}

1.
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
2.
Key Laboratory of Intelligent Information Processing, Shandong Normal University, Jinan 250358, China
3.
College of Jinan Institute of Technology, Jinan 250031, China
4.
School of Design, The Hong Kong Polytechnic University, Hong Kong 999077, China

Funds: The National Natural Science Foundation of China (61902225, 61502285), The Natural Science Foundation of Shandong Province (ZR2014FQ013)

摘要

摘要: 图像缩放技术要求对图像缩放的同时保证重要信息不丢失且物体边缘不发生扭曲。近年来，Seam Carving及其改进算法得到了广泛的关注和研究。由于采用了离散式最小能量线迭代搜索策略，缩放信息无法在迭代过程中传递导致扭曲现象普遍存在。该文针对上述问题提出最小位移可视差(JND)检测算法，能够有效地检测每一次迭代中出现的潜在扭曲信息。能量权重$ {E}_{w} $能够将JND信息累加传递给后续的迭代过程，从而抑制缩放过程中的边缘扭曲现象。通过JND算法和能量权重，该文首次将离散的Seam Carving模型转变为连续缩放模型。最后，在公共数据集RetargetMe上与最新的图像缩放算法进行多组对比实验，验证了所提方法的有效性和先进性。
- 图像缩放 /
- Seam Carving /
- 最小位移可视差 /
- 平均场近似
Abstract: Image retargeting technologies require important information preservation and less edge distortion during increasing/decreasing image size. The seam carving based algorithms, as the classic retargeting model, receive widespread attention in recent years. However, because of the discrete least energy seam searching strategy, the retargeting information can not be passed generation by generation, which causes retargeting distortions to prevail. To solve this problem, the Just Noticeable Distortion (JND) algorithm is proposed to detect the potential distribution of distortion information. Through the proposed energy weight E_w, the JND information can be passed to the following retargeting iteration for distortion reduction. According to the best knowledge, it is the first time to propose the seam carving algorithm in continuous way by the JND algorithm and energy weight, are the promising results also demonstrated compared with several new approaches at public database ‘Retarget Me’, qualitatively and quantitatively.
- Image Retargeting /
- Seam Carving /
- Just Noticeable Distortion (JND) /
- Mean Approximation

HTML全文

图 1 传统SC模型与本文模型对比图

下载: 全尺寸图片幻灯片

图 2 连续SC缩放模型处理流程

下载: 全尺寸图片幻灯片

图 3 SC改进算法定量对比实验

下载: 全尺寸图片幻灯片

图 4 像素级扭曲度对比

下载: 全尺寸图片幻灯片

图 5 图像缩放算法定量对比实验

下载: 全尺寸图片幻灯片

表 1 连续Seam Carving图像缩放算法

输入：目标图像, I_in，缩放比例参数, n
输出：缩放结果图像，I_out
1: repeat
2: 根据式(11)计算${f_{{\rm{en}}}}$
3: 根据式(12)计算$E_{{w}}^{\rm{i}}$
4: 微小缩放I₂和I₁至相同大小
5: 根据式(9)和式(10)计算u_i
6: 进行缝合计算
7: 更新缩放参数
8: until seam_num=n

下载: 导出CSV

表 2 图像缩放定量对比

	Crop	SC	SNS	WARP	DISJND	CONJND
Sift-flow	0.37	0.57	0.69	0.38	0.59	0.79
ARS	0.94	0.89	0.90	0.89	0.89	0.91
Vssm	0.25	0.68	0.68	0.75	0.65	0.42

下载: 导出CSV

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