Image Quality Enhancement Using Pixel-wise Gamma Correction via Svm Classifier

value of its individual pixels. Most of existing gamma correction methods apply a uniform gamma value across the image. Considering the fact that gamma variation for a single image is actually nonlinear, the proposed method locally estimates the gamma values in an image database of training images are constructed from various standard images under different gamma conditions. Then by windowing each of the training images, a number of features that characterize images content are computed from its pixel intensity histogram, gray level co-occurrence matrix, and discrete cosine transform domain. To improve the gamma values of an image the aforementioned features are initially computed in sliding windows, then SVM is employed to estimate the gamma value in each window. In this study, it is shown that the proposed method has performed well in improving the quality of images. Subjective and objective image quality assessments used in this study attest superiority of the proposed method compared to the existing methods in image quality enhancement using image gamma value.