Abstract
D.Ing.
The rapid use of digital images and the necessity to compress them, has created the need for the development of image quality metrics. Subjective evaluation is the most accurate of the image quality evaluation methods, but it is time consuming, tedious and expensive. In the mean time widely used objective evaluations such as the mean squared error measure has proven that they do not assess the image quality the way a human observer does. Since the human observer is the final receiver of most visual information, taking the way humans perceive visual information will be greatly beneficial for the development of an objective image quality metric that will reflect the subjective evaluation of distorted images. Many attempts have been carried out in the past, which tried to develop distortion metrics that model the processes of the human visual system, and many promising results have been achieved. However most of these metrics were developed with the use of simple visual stimuli, and most of these models were based on the visibility threshold measures, which are not representative of the distortion introduced in complex natural compressed images.
In this thesis, a new image quality metric based on the human visual system properties as related to image perception is proposed. This metric provides an objective image quality measure for the subjective quality of coded natural images with suprathreshold degradation. This proposed model specifically takes into account the structure of the natural images, by analyzing the images into their different components, namely: the edges, texture and background (smooth) components, as these components influence the formation of perception in the HVS differently. Hence the HVS sensitivity to errors in images depends on weather these errors lie in more active areas of the image, such as strong edges or texture, or in the less active areas such as the smooth areas. These components are then summed to obtain the combined image which represents the way the HVS is postulated to perceive the image.
Extensive subjective evaluation was carried out for the different image components and the combined image, obtained for the coded images at different qualities. The objective (RMSE) for these images was also calculated. A transformation between the subjective and the objective quality measures was performed, from which the objective metric that can predict the human perception of image quality was developed.
The metric was shown to provide an accurate prediction of image quality, which agrees well with the prediction provided by the expensive and lengthy process of subjective evaluation. Furthermore it has the desired properties of the RMSE of being easier and cheaper to implement. Therefore, this metric will be useful for evaluating error mechanisms present in proposed coding schemes.