Prior versions of reversible data hiding with contrast enhancement(RDHCE)algorithms strongly focused on enhancing the contrast of grayscale images.However,RDHCE has recently witnessed a rise in contrast enhance-ment a...Prior versions of reversible data hiding with contrast enhancement(RDHCE)algorithms strongly focused on enhancing the contrast of grayscale images.However,RDHCE has recently witnessed a rise in contrast enhance-ment algorithms concentrating on color images.This paper implies a method for color images that uses the RGB(red,green,and blue)color model and is based on bi-histogram shifting and image adjustment.Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final enhanced image.Images are first divided into three channels-R,G,and B-and the Max,Med,and Min channels are then determined from these.Before histogram shifting,some calculations are done to determine how many iterations there will be for each channel.The images are adjusted to improve visual quality in the enhanced images after data has been embedded in each channel.The experimental results show that the enhanced images produced by the proposed method are qualitatively and aesthetically superior to those produced by some earlier methods,and their quality was assessed using PSNR,SSIM,RCE,RMBE,and CIEDE2000.The embedding rate obtained by the suggested method is acceptable.展开更多
Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,gr...Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,green,and blue)color model.The proposed method selects the two highest bins and two lowest bins from the image histogram,performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images.The proposed method simultaneously performs both right histogram shifting(RHS)and left histogram shifting(LHS)in each histogram shifting repetition to embed and split the highest bins while combining the lowest bins with their neighbors to achieve histogram equalization(HE).The least maximum number of histograms shifting repetitions among the three RGB channels is used as the default number of histograms shifting repetitions performed to enhance original images.Compared to an existing contrast enhancement method for color images and evaluated with PSNR,SSIM,RCE,and RMBE quality assessment metrics,the experimental results show that the proposed method's enhanced images are visually and qualitatively superior with a more evenly distributed histogram.The proposed method achieves higher embedding capacities and embedding rates in all images,with an average increase in embedding capacity of 52.1%.展开更多
为改进亮度保持双直方图均衡算法的不足,提出基于最大熵模型的动态范围优化方法,扩展了双直方图均衡算法的应用范围,使之不仅适用于正常亮度图像,对低照度及高亮图像也能取得较好的效果.算法首先选用大津法确定直方图数据分割点;然后对...为改进亮度保持双直方图均衡算法的不足,提出基于最大熵模型的动态范围优化方法,扩展了双直方图均衡算法的应用范围,使之不仅适用于正常亮度图像,对低照度及高亮图像也能取得较好的效果.算法首先选用大津法确定直方图数据分割点;然后对初始直方图进行预处理;根据所提出的最大熵模型确定最佳的动态范围分割点;最后进行双直方图均衡得到增强图像.本文选取多个图像数据库进行测试,并与BBHE(Brightness preserving Bi-Histogram Equalization)、BPCLBHE(Brightness Preserving and Contrast Limited Bi-Histogram Equalization)、ESIHE(Exposure based Sub Image Histogram Equalization)和DRSHE(Dynamic Range Separate Histogram Equalization)进行比较,同时将信息熵、对比度和NIQE(Natural Image Quality Evaluator)作为客观评价指标.实验结果证明,本文算法对各类图像均具有较好的主观视觉效果和客观评价指标,在保留细节的同时兼顾了对比度的增强.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant No.61662039in part by the Jiangxi Key Natural Science Foundation under No.20192ACBL20031+1 种基金in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST)under Grant No.2019r070in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Prior versions of reversible data hiding with contrast enhancement(RDHCE)algorithms strongly focused on enhancing the contrast of grayscale images.However,RDHCE has recently witnessed a rise in contrast enhance-ment algorithms concentrating on color images.This paper implies a method for color images that uses the RGB(red,green,and blue)color model and is based on bi-histogram shifting and image adjustment.Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final enhanced image.Images are first divided into three channels-R,G,and B-and the Max,Med,and Min channels are then determined from these.Before histogram shifting,some calculations are done to determine how many iterations there will be for each channel.The images are adjusted to improve visual quality in the enhanced images after data has been embedded in each channel.The experimental results show that the enhanced images produced by the proposed method are qualitatively and aesthetically superior to those produced by some earlier methods,and their quality was assessed using PSNR,SSIM,RCE,RMBE,and CIEDE2000.The embedding rate obtained by the suggested method is acceptable.
基金supported in part by the National Natural Science Foundation of China under Grant No.61662039in part by the Jiangxi Key Natural Science Foundation under No.20192ACBL20031+1 种基金in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST)under Grant No.2019r070in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,green,and blue)color model.The proposed method selects the two highest bins and two lowest bins from the image histogram,performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images.The proposed method simultaneously performs both right histogram shifting(RHS)and left histogram shifting(LHS)in each histogram shifting repetition to embed and split the highest bins while combining the lowest bins with their neighbors to achieve histogram equalization(HE).The least maximum number of histograms shifting repetitions among the three RGB channels is used as the default number of histograms shifting repetitions performed to enhance original images.Compared to an existing contrast enhancement method for color images and evaluated with PSNR,SSIM,RCE,and RMBE quality assessment metrics,the experimental results show that the proposed method's enhanced images are visually and qualitatively superior with a more evenly distributed histogram.The proposed method achieves higher embedding capacities and embedding rates in all images,with an average increase in embedding capacity of 52.1%.
文摘为改进亮度保持双直方图均衡算法的不足,提出基于最大熵模型的动态范围优化方法,扩展了双直方图均衡算法的应用范围,使之不仅适用于正常亮度图像,对低照度及高亮图像也能取得较好的效果.算法首先选用大津法确定直方图数据分割点;然后对初始直方图进行预处理;根据所提出的最大熵模型确定最佳的动态范围分割点;最后进行双直方图均衡得到增强图像.本文选取多个图像数据库进行测试,并与BBHE(Brightness preserving Bi-Histogram Equalization)、BPCLBHE(Brightness Preserving and Contrast Limited Bi-Histogram Equalization)、ESIHE(Exposure based Sub Image Histogram Equalization)和DRSHE(Dynamic Range Separate Histogram Equalization)进行比较,同时将信息熵、对比度和NIQE(Natural Image Quality Evaluator)作为客观评价指标.实验结果证明,本文算法对各类图像均具有较好的主观视觉效果和客观评价指标,在保留细节的同时兼顾了对比度的增强.
