Quality Assessment of Post-Processed Images

Abstract

The vast majority of the work done in the field of quality assessment during last two decades has been dedicated to the quantification of the distortion caused by the processing of an image. The original image was, therefore, always considered to be of the best possible quality. In this kind of scenario, the notion of quality can be expressed as the fidelity of the processed version to the reference. However, some post-processing algorithms enable to adjust aesthetic properties of an image in order to enhance the perceived quality. In such cases, the best possible quality image is not available and the classical fidelity approach is no longer applicable. The goal of this thesis is to revise the quality assessment methodologies to cope with the challenges brought by the post-processing into the quality evaluation. The post-processing algorithms, relevant to the topic of this thesis, come from two groups – image enhancement, represented by image sharpening, and dynamic range compression (also known as tone-mapping) techniques. Both subjective and objective quality assessment methodologies applicable in these areas are studied and the suitable solutions, outperforming the state-of-the-art methods, are proposed. Moreover, a novel methodology for evaluating the performance of objective quality metrics, overcoming the shortcomings of the currently available methods, is presented.