基于HVS的水下图像质量评价  

Underwater Image Quality Assessment Based on HVS

作　　者：鹿婷 侯国家 潘振宽 王国栋 LU Ting;HOU Guo-jia;PAN Zhen-kuan;WANG Guo-dong(College of Computer Science and Technology,Qingdao University,Qingdao,Shandong 266071,China)

机构地区：[1]青岛大学计算机科学技术学院,山东青岛266071

出　　处：《计算机科学》2022年第5期98-104,共7页Computer Science

基　　金：国家自然科学基金(61901240);山东省自然科学基金(ZR2019BF042,ZR2019MF050);中国国家留学基金(201908370002);中国博士后科学基金(2017M612204)。

摘　　要：因为水的吸收和散射效应,导致水下图像普遍存在模糊、低对比度和色彩不均衡等问题,而自然图像质量评价方法没有考虑水下成像的特殊性,难以应用于水下图像;同时目前有效的水下图像的质量评价方法较少,且存在一定局限性。针对此问题,提出了一种新的与主观感知密切相关的无参考水下图像质量评价方法,选择与视觉感知相关性高的色度特征(Col)、基于人类大脑视觉皮层的对比度特征(Con)、反映图像信息丰富程度的清晰度特征(Sharp)这3种属性,来构成水下图像质量评价模型,简称CCS。这些视觉特征对水的物理特性比较敏感,而且人类视觉系统(Human Visual System,HVS)易受色彩、对比度和边缘结构等视觉特性变化的影响。为了验证所提方法的性能,在自建小型水下图像数据集上与CPDB,BRISQUE,UCIQE,UIQM这4种无参考评价算法进行了大量的对比实验,在与主观评价相关性度量方面,CCS方法比UIQM方法的RMSE度量指标提升了大约13%,比UCIQE和UIQM方法的PLCC,SROCC和KROCC度量指标提升均超过10%。实验结果表明,CCS算法与人类视觉感知具有高度一致性,能有效、准确地评估水下图像的质量。Due to the absorption and scattering effects under water,underwater image often suffers from blurring,low contrast,color casting and so on.The degraded images will decline the accuracy and effectiveness in underwater archaeology,marine ecological research,underwater target detection and tracking.On the other hand,underwater image quality assessment plays a key goal in the development and exploration of the ocean.An effective underwater image quality evaluation system can provide a gui-dance for optimizing underwater enhancement and restoration algorithms and promote the progress of underwater vision.Therefore,it is desire to design an effective and robust algorithm for underwater image quality evaluation.Since the atmospheric image quality evaluation methods don’t consider the characteristics of the water absorption of light,they aren’t suitable for evaluating underwater image quality.Additionally,there are few effective metrics for underwater images quality evaluation up to now.To address this problem,we propose a new no-reference underwater image quality measure containing color index,contrast,and sharpness indexes,dubbed CCS,which has stronger correlation with human subjective perception.These attributes not only are sensitive to the physical characteristics of the water,but also the human visual system(HVS)is sensitive to the changes of the visual properties such as color,contrast,and edge structures.To verify the performance of the proposed CCS,we conduct considerable experiments on a small underwater image dataset comparing with the other four non-reference metrics including CPBD,BRISQUE,UIQM and UCIQE.It can be seen that our CCS metric is higher about 13%than UIQM in terms of RMSE,moreover,is higher above 10%than UIQM and UCIQE in terms of PLCC,SROCC,and KROCC.Experimental results demonstrate that the proposed CCS metric has a higher correlation with subjective evaluations,which can effectively and accurately evaluate the underwater image quality.

关 键 词：图像质量评价 无参考 人类视觉系统 色度评估 对比度评估 清晰度评估 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]