A framework for measuring the effectiveness of image representations

Image representations are abstract descriptions of images. They refer to ideas describing or modelling images, in a particular way, for further processing and analysis. For a given image application and a given image, there are various image representations that can be employed. It is desired that the best image representation is used. The best image representation minimises storage size while preserving maximum image information. The thesis proposes a framework to evaluate the effectiveness of image representations. The effectiveness of image representations is measured by their storage size and preserved image information, with respect to a given image application. In consequence, the proposed framework allows us to seek the best image representation for a given image. In the proposed framework, different image representations for a given image are realised by their corresponding image transformations. This step generates different sets of coefficients for different image representations. Next, methods for ordering coefficients in each set are employed to sort coefficients from most-to-least significant. This step allows us to obtain the best subset of coefficients in each representation, by selecting the most significant coefficients until the required storage is met. Then, an appropriate quality measure is applied to select the best subset of coefficients. The best subset of coefficients corresponds to the best image representation, for a given image. This thesis also proposes a quality measure for image representations. It is based on the perceptual quality evaluation of image representations. Experimental results have shown that the proposed quality measure correlates well with evaluation by the human visual system. Finally, a validation of the proposed framework is carried out. Following the proposed framework, a method to obtain the best wavelet representation is introduced. Given a set of wavelets to represent an image, the proposed method enables us to attain the best wavelet representation, in terms of the highest perceptual image quality for a given storage size. The best wavelet image representation can be used to gain better performance for image coding, image enhancement and image comparison applications.