Color space identification from single images
In this paper, we focus on the problem of RGB color space identification from a single image. At the moment, RGB color spaces are widely adopted in photography for image producing. The problems with respect to color space identification, such as to get the consistent printing or displaying quality on screen devices and software applications and prevention of multimedia unauthorized usage(shown or printed by other device via gamut mapping), need to be concerned. Current techniques are all relying on EXchangeable Image File Format (EXIF) to extract color space information. In this paper, we use a two-dimensional non-causal regressive model to explore the image demosaicing properties in order to extract discriminative features and train them on SVM classifier for image color space detection without relying on EXIF. In our experiment, images in three different color spaces (sRGB, adobeRGB and pro PhotoRGB) are generated for color space identification task. The experimental results show that the proposed technique has an good performance on image color space identification.