Description

Gray scale image contains pixels which are not a RGB color pixels. Many applications convert a gray scale image into RGB color space but fail to preserve the original contents of a Gray Scale image. The important criteria used in subjective evaluation of distorted images include the amount of distortion, the type of distortion, and the distribution of error. An ideal image quality measure should therefore be able to mimic the human observer. Conventional algorithms have been widely used in color to gray applications, for example, in publishing as a less expensive alternative to full color images, making color blind people perceive visual cues better in color images, and enhancement for the e-ink based book reader, which can only support grayscale rendering currently. Color to gray algorithms are used to convert a color image to a grayscale one while preserving important visual cues. In the existing system, C2G conversion algorithms seek to preserve color distinctions of the input color image in the corresponding grayscale image with some additional constraints, such as global consistency and grayscale preservation. In this project the system proposed a C2G structural similarity (C2G-SSIM) index. It evaluates the luminance, contrast, and structure similarities between the reference color image and the C2G converted image. The three components are then integrated into an overall quality measure based on the type of content (photographic or synthetic) in the image. The system measure luminance, contrast and structure distortions to capture perceived quality changes introduced by C2G conversion. The system compare C2G-SSIM with existing metrics RWMS, CCPR, CCFR and E-score.