{"title":"稀疏颜色直方图图像的不可分加权中值分割量化","authors":"W. Sae-Tang, Mika Sugiyama, M. Fujiyoshi, H. Kiya","doi":"10.1109/ISPACS.2012.6473536","DOIUrl":null,"url":null,"abstract":"Non-separable color reduction for color images is proposed in this paper. The proposed method befits high bit rate quantization in which, for example, a 24 bits per pixel (bpp) color image is quantized to an image with 18 bpp or more, while the conventional median-cut quantization gives huge quantization errors in such condition. This feature is useful in bit depth conversion for modern high bit depth images and is also useful in visually lossless compression. Moreover, by taking into account color histogram sparseness in which a few colors among huge possible colors are used, lossless quantization with a reasonable processing time is served by the proposed method, whereas the conventional vector quantization requires numerous processing time for lossless quantization.","PeriodicalId":158744,"journal":{"name":"2012 International Symposium on Intelligent Signal Processing and Communications Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-separable weighted median-cut quantization for images with sparse color histogram\",\"authors\":\"W. Sae-Tang, Mika Sugiyama, M. Fujiyoshi, H. Kiya\",\"doi\":\"10.1109/ISPACS.2012.6473536\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Non-separable color reduction for color images is proposed in this paper. The proposed method befits high bit rate quantization in which, for example, a 24 bits per pixel (bpp) color image is quantized to an image with 18 bpp or more, while the conventional median-cut quantization gives huge quantization errors in such condition. This feature is useful in bit depth conversion for modern high bit depth images and is also useful in visually lossless compression. Moreover, by taking into account color histogram sparseness in which a few colors among huge possible colors are used, lossless quantization with a reasonable processing time is served by the proposed method, whereas the conventional vector quantization requires numerous processing time for lossless quantization.\",\"PeriodicalId\":158744,\"journal\":{\"name\":\"2012 International Symposium on Intelligent Signal Processing and Communications Systems\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Symposium on Intelligent Signal Processing and Communications Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPACS.2012.6473536\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Symposium on Intelligent Signal Processing and Communications Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPACS.2012.6473536","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Non-separable weighted median-cut quantization for images with sparse color histogram
Non-separable color reduction for color images is proposed in this paper. The proposed method befits high bit rate quantization in which, for example, a 24 bits per pixel (bpp) color image is quantized to an image with 18 bpp or more, while the conventional median-cut quantization gives huge quantization errors in such condition. This feature is useful in bit depth conversion for modern high bit depth images and is also useful in visually lossless compression. Moreover, by taking into account color histogram sparseness in which a few colors among huge possible colors are used, lossless quantization with a reasonable processing time is served by the proposed method, whereas the conventional vector quantization requires numerous processing time for lossless quantization.