Consistency Enhancement Quality Assessment Criterion in Confidence Interval for Image Set
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摘要:
在对整个图像集进行增强质量评价时，现有的平均准则会随着不同图像集非一致性地变化，从而导致较大的评价质量波动。为此，该文提出一个面向图像集的置信区间内一致性增强质量评价准则，通过设置应用参数并使用置信区间筛选数据，再比较各图像增强前后的质量分数差值，由此评估图像质量增强的一致性，最终计算出一致性增强质量分数有效值。在众多图像增强算法中，所提准则能够挑选出具体应用所需要的稳定性强、可靠性高的增强算法。实验结果表明，所提准则具有良好的主客观评价一致性，性能优于当前的平均准则，为各种图像增强算法提供了一个可用于任意图像集的质量评价准则。
Abstract:When evaluating the enhancement quality of a whole image set, the existing average score criterion varies inconsistently with different image sets and produces a large evaluation quality fluctuation. Therefore, this paper proposes a consistency enhancement quality assessment criterion in confidence interval for any image set. By setting application parameters and using confidence interval to screen data, the proposed criterion compares the quality score difference before and after enhancing each image, and evaluates the consistency of image quality enhancement, and then calculates the effective value of consistency enhancement quality scores. Among many image enhancement algorithms, the proposed criterion can select the high-reliability enhancement algorithm for a specific application. The experimental results show that the proposed criterion has good subjective and objective consistency and outperforms the existing average score criterion, which provides an evaluation criterion for those image enhancement algorithms applied to any image set.
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Key words:
- Image enhancement /
- Image set /
- Quality assessment /
- Consistency enhancement /
- Confidence interval
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王志明. 无参考图像质量评价综述[J]. 自动化学报, 2015, 41(6): 1062–1079. doi: 10.16383/j.aas.2015.c140404WANG Zhiming. Review of no-reference image quality assessment[J]. Acta Automatica Sinica, 2015, 41(6): 1062–1079. doi: 10.16383/j.aas.2015.c140404 PENG Y T and COSMAN P C. Underwater image restoration based on image blurriness and light absorption[J]. IEEE Transactions on Image Processing, 2017, 26(4): 1579–1594. doi: 10.1109/TIP.2017.2663846 LI Chongyi, GUO Jichang, CONG Runmin, et al. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior[J]. IEEE Transactions on Image Processing, 2016, 25(12): 5664–5677. doi: 10.1109/TIP.2016.2612882 LI Chongyi, GUO Jichang, CHEN Shanji, et al. Underwater image restoration based on minimum information loss principle and optical properties of underwater imaging[C]. IEEE International Conference on Image Processing, Phoenix, USA, 2016: 1993–1997. YANG M and SOWMYA A. An underwater color image quality evaluation metric[J]. IEEE Transactions on Image Processing, 2015, 24(12): 6062–6071. doi: 10.1109/TIP.2015.2491020 吴雪垠, 吴谨, 张鹤. 逆滤波法在图像复原中的应用[J]. 信息技术, 2011(10): 183–185. doi: 10.3969/j.issn.1009-2552.2011.10.050WU Xueyin, WU Jin, and ZHANG He. Research on image restoration techniques based on inverse filtering algorithm[J]. Information Technology, 2011(10): 183–185. doi: 10.3969/j.issn.1009-2552.2011.10.050 何石, 潘晓璐, 李一民. 一种均值滤波的优化算法[J]. 信息技术, 2012(3): 133–137. doi: 10.3969/j.issn.1009-2552.2012.03.038HE Shi, PAN Xiaolu, and LI Yimin. Optimization algorithm for average filtering[J]. Information Technology, 2012(3): 133–137. doi: 10.3969/j.issn.1009-2552.2012.03.038 苏志锋. 基于FPGA的图像预处理研究与实现[D]. [博士论文], 华南理工大学, 2015.SU Zhifeng. Studying and implementation of image signal preprocessing based on FPGA[D]. [Ph.D. dissertation], South China University of Technology, 2015. 李耀辉, 刘保军. 基于直方图均衡的图像增强[J]. 华北科技学院学报, 2003, 5(2): 65–67.LI Yaohui and LIU Baojun. The image enhancement based on histogram equalization[J]. Journal of North China Institute of Science and Technology, 2003, 5(2): 65–67. HITAM M S, AWALLUDIN E A, and YUSSOF W. Mixture contrast limited adaptive histogram equalization for underwater image enhancement[C]. International Conference on Computer Application Technology, Sousse, Tunisia, 2013: 1–5. 陈宇, 霍富荣, 苗华. 对比度拉伸在目标探测与识别中的应用研究[J]. 仪器仪表学报, 2008, 29(4): 795–798.CHEN Yu, HUO Furong, and MIAO Hua. Application of contrast stretching in optical correlation detection and recognition[J]. Chinese Journal of Scientific Instrument, 2008, 29(4): 795–798. 杨勇, 郭玲, 王天江. 基于多尺度结构张量的多类无监督彩色纹理图像分割方法[J]. 计算机辅助设计与图形学学报, 2014, 26(5): 812–825.YANG Yong, GUO Ling, and WANG Tianjiang. Multi-scale structure tensor based unsupervised color-texture image segmentation approach in multiclass[J]. Journal of Computer-Aided Design &Computer Graphics, 2014, 26(5): 812–825. 蒋刚毅, 黄大江, 王旭, 等. 图像质量评价方法研究进展[J]. 电子与信息学报, 2010, 32(1): 219–226. doi: 10.3724/SP.J.1146.2009.00091JIANG Gangyi, HUANG Dajiang, WANG Xu, et al. Overview on image quality assessment methods[J]. Journal of Electronics &Information Technology, 2010, 32(1): 219–226. doi: 10.3724/SP.J.1146.2009.00091 EMBERTON S, CHITTKA L, and CAVALLARO A. Hierarchical rank-based veiling light estimation for underwater dehazing[C]. British Machine Vision Conference, Swansea, UK, 2015: 125.1–125.12. JIAN Muwei, QI Qiang, DONG Junyu, et al. The OUC-Vision large-scale underwater image database[C]. IEEE International Conference on Multimedia & Expo, Hong Kong, China, 2017: 1297–1302. JIAN Muwei, QI Qiang, DONG Junyu, et al. Saliency detection using quaternionic distance based weber local descriptor and level priors[J]. Multimedia Tools and Applications, 2018, 77(11): 14343–14360. doi: 10.1007/s11042-017-5032-z -
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