No-reference image quality assessment in contourlet domain

In image processing, efficiency term refers to the ability in capturing significant information that is sensitive to human visual system with small description. Natural images or scenes that contain intrinsic geometrical structures (contours) are key features of visual information. The existing transform methods like Fourier transformation, wavelets, curvelets, ridgelets etc., have limitations in capturing directional information in an image and their compatibility with compression methods. Hence, to capture the directional information or natural scene statistics of an image and to handle the compatibility over distortion methods, Contourlet Transform (CT) can be a promising approach. The goal of no-reference image quality assessment using contourlet transform (NR IQACT) is to establish a rational computational model to predict the visual quality of an image. In this thesis we implemented an improved Natural Scene Statistics (NSS) model that blindly measures image quality using the concept of Contourlet Transform (CT). In fact, natural scenes contain nonlinear dependencies that can be disturbed by a compression process. This disturbance can be quantified and related to human perception of quality.