Color Image Modification with and without Hue Preservation

Abstract

Color image modification is an essential component for several applications and the grayscale transformation is generally mapped to the color image indirectly. Although several techniques have been used for this transfer, they suffer from gamut mapping issue. In this paper, it is aimed to map the transformation in an accurate manner while preserving hue. Modifying the image in different color space than the original retains hue to a promising extent, but suffers from the gamut problem. A generic scheme to map grayscale changes to the color space for all kinds of spatial modification is proposed here. The hue preserving color image enhancement (HPCE) scheme discussed here is free of gamut-mapping issue and shows promising results in transferring the grayscale transformation to the color image in a simplistic manner. The proposed HPCE scheme is analysed qualitatively through visual appearance and quantitatively using color difference metrics SHAME and CID, gray image difference and EBCM measures. Different gray scale transformations such as S-type enhancement and different forms of histogram equalization techniques are applied on Berkeley dataset of 500 images to prove the efficacy of proposed algorithm.

Near Hue-Preserving Reversible Contrast and Saturation Enhancement Using Histogram Shifting

“…Subsequently, using Eq. ( 10) or (11), the histogram is shifted, and the side information is embedded. It is noteworthy that 16 pixels in the upper left of the image were excluded when creating the histogram in this process to guarantee reversibility.…”

“…In addition, there is an application field such as the field using of digital photography as a legal evidence in which the reversibility of the color image is desirable, and the guarantee of reversibility can reduce the storage space required for color images and the risk of evidence management failures. However, most existing enhancement methods cannot restore the original image from an enhanced image [7]- [11].…”

Conversion of Landsat 8 multispectral data through modified private content‐based image retrieval technique for secure transmission and privacy

“…There are several methods described to separate luminance information from hue and saturation, for example, YIQ, HSV, LHS, CIELab, 35,36 and so on. The grayscale images store luminance information effectively within themselves, whereas the multispectral images are converted to grayscale by preserving the image “contrast.” Here, we are explaining the conversion procedure of multispectral image to grayscale using the “Di Zenzo Structure tensor matrix” 37,38 …”