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一种基于HSV色彩空间的OLED低功耗方法 被引量:1

A low-power approach for OLED displays based on HSV color space
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摘要 为了降低有机发光器件(OLED)的功耗,同时又保证显示图像的质量,提出一种基于色调、饱和度和亮度(HSV,hue,saturation and value)色彩空间的OLED低功耗方法。通过整合亮度调节(VS)和饱和度调节(SS)降低显示图像的功耗,应用感兴趣区域(ROI,region of interest)到图像边界逐级调节技术提升图像质量,采用平均结构相似度指数(MSSIM,mean structural similarity index)对原始输入图像和输出图像在亮度、对比度和结构相似性上进行比较以评价图像相似程度,达到保持较高图像质量的同时降低功耗的目的。实验结果表明,图像渐变级数level为15时,本文提出的亮度与饱和度整合调节(VSI)方法在保持图像的ROI具有80.31%的相似度的同时节省了57.93%的OLED功耗。 In order to reduce the power consumption of ()LED displays and guarantee image quaiity at the same time, a new OLED low power approach based on hue, saturation and value (HSV) color space is proposed. The approach applies value and saturation scaling (SS) integration technique to save display power. Further,the original input image is divide into two area,i, e. ,region oF interest (ROD and region of non-interest,and then a technique, gradually decreasing the value and increasing the saturation from ROI to the edge,is applied to improve image quality which can be evaluated by the mean structural simi- larity index (MSSIM). The MSSIM quality metric is widely used to evaluate the amount of image dis- tortion and adopted to account for the human visual perception among the image processing community, which divides the original image and the output image into several windows and compares the luminance, the contrast and the structural similarity iwth the other windows. As a result,obvious of power OLED displays is saved without significantly affecting the perceived image quality. The experimental results show that the proposed approach keeps the MSSIM at a high level of 80.31% and save 57.93% OLED power when the gradual level equals 15.
出处 《光电子.激光》 EI CAS CSCD 北大核心 2013年第10期1878-1883,共6页 Journal of Optoelectronics·Laser
基金 国家自然科学基金(61332001 61272104 61073045) 四川省杰出青年基金(2010JQ0011) 中国科学院计算技术研究所计体系结构国家重点实验室(ICT-ARCH2010003) 四川省教育厅科研(10ZB146) 成都大学科研(2010XJZ25)资助项目
关键词 色彩空间 功耗管理 饱和度调节(SS) 平均结构相似度指数(MSSIM) color space power management saturation scaling mean structural similarity index(MSSIM)
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