{"title":"A novel method of combining pixel density map and SLIC for low-power display","authors":"Simon Suh, Young-jin Kim","doi":"10.1109/ICTC49870.2020.9289228","DOIUrl":null,"url":null,"abstract":"OLED displays have different power consumption depending on R, G, and B pixel values. Therefore, if an image is segmented according to saliency and then divided according to color using a super pixel algorithm, low power can be achieved while maintaining human visual satisfaction However, if the image is segmented using saliency and then the segmented image is segmented using the super pixel algorithm, simple linear iterative clustering(SLIC), the pixels that do not have a color value because the segmented image has a different saliency level are also segmented by the super pixel algorithm. So the ability to divide color is poor at segmented image. This paper excludes pixels that do not have color values from the segmentation process when dividing an image including pixels that do not have color values by saliency criteria into super pixels. In addition, by allocating the first search position not evenly in the entire image, but focusing on the pixels with color values, the performance of the super pixel that divides the image according to color in the image divided based on saliency was improved. In terms of low power, the proposed method has similar power savings of about 38% to that of the FDM-oriented SLIC method, but the SSIM, which is structurally similar to the original image, has shown higher.","PeriodicalId":282243,"journal":{"name":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Information and Communication Technology Convergence (ICTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTC49870.2020.9289228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
OLED displays have different power consumption depending on R, G, and B pixel values. Therefore, if an image is segmented according to saliency and then divided according to color using a super pixel algorithm, low power can be achieved while maintaining human visual satisfaction However, if the image is segmented using saliency and then the segmented image is segmented using the super pixel algorithm, simple linear iterative clustering(SLIC), the pixels that do not have a color value because the segmented image has a different saliency level are also segmented by the super pixel algorithm. So the ability to divide color is poor at segmented image. This paper excludes pixels that do not have color values from the segmentation process when dividing an image including pixels that do not have color values by saliency criteria into super pixels. In addition, by allocating the first search position not evenly in the entire image, but focusing on the pixels with color values, the performance of the super pixel that divides the image according to color in the image divided based on saliency was improved. In terms of low power, the proposed method has similar power savings of about 38% to that of the FDM-oriented SLIC method, but the SSIM, which is structurally similar to the original image, has shown higher.
OLED显示器的功耗根据R、G、B像素值的不同而不同。因此,如果先对图像进行显著性分割,再使用超像素算法对图像进行颜色分割,可以在保持人眼视觉满意度的同时实现低功耗。然而,如果先对图像进行显著性分割,再使用超像素算法对分割后的图像进行简单线性迭代聚类(simple linear iterative clustering, SLIC),由于分割的图像具有不同的显着性水平而不具有颜色值的像素也由超像素算法分割。因此,分割图像的颜色分割能力较差。本文在将包含不具有颜色值的像素的图像按显著性标准划分为超级像素时,将不具有颜色值的像素从分割过程中排除。此外,通过在整个图像中不均匀地分配第一个搜索位置,而是集中在具有颜色值的像素上,提高了基于显著性划分的图像中根据颜色划分图像的超级像素的性能。在低功耗方面,所提出的方法与面向fdm的SLIC方法的功耗节省相似,约为38%,但与原始图像结构相似的SSIM显示出更高的功耗。