Abstract
Image quality (IQ) can be degraded due to various types of distortion such as noise, blurring, fast fading (FF), blocking artifacts and contrast distortion. These distortions may occur during operations such as acquisition, compression, storage, transmission, display and post-processing. Contrast distortion is one of the most common types of distortion. Contrast-distorted image (CDI) is defined as image with low dynamic range of brightness. Most of existing works that used discrete curvelet transform (DCT) are focused on image distortions such as blur, noise, and compression which often affect the high frequency components of an image. Therefore, this paper will study and investigate the application of curvelet transform for CDI. The distributions of curvelet coefficients at different scales have been found to be effective in characterizing good contrast image and contrast-distorted image. The distributions of curvelet coefficients at the coarsest and finest scales can be used to derive potential features in characterizing good contrast images and also contrast-distorted images.