Graduation Year


Document Type




Degree Granting Department

Electrical Engineering

Major Professor



image coding, image quality measure, human visual system, contrast sensitivity function, brightness perception


Image quality measures are used to optimize image processing algorithms and evaluate their performances. The only reliable way to assess image quality is subjective evaluation by human observers, where the mean value of their scores is used as the quality measure. This is known as mean opinion score (MOS). In addition to this measure there are various objective (quantitative) measures. Most widely used quantitative measures are: mean squared error (MSE), peak signal to noise ratio (PSNR) and signal to noise ratio (SNR). Since these simple measures do not always produce results that are in agreement with subjective evaluation, many other quality measures have been proposed. They are mostly various modifications of MSE, which try to take into account some properties of human visual system (HVS) such as nonlinear character of brightness perception, contrast sensitivity function (CSF) and texture masking.

In these approaches quality measure is computed as MSE of input image intensities or frequency domain coefficients obtained after some transform (DFT, DCT etc.), weighted by some coefficients which account for the mentioned properties of HVS. These measures have some advantages over MSE, but their ability to predict image quality is still limited. A different approach is presented here. Quality measure proposed here uses simple model of HVS, which has one user-defined parameter, whose value depends on the reference image. This quality measure is based on the average value of locally computed correlation coefficients. This takes into account structural similarity between original and distorted images, which cannot be measured by MSE or any kind of weighted MSE. The proposed measure also differentiates between random and signal dependant distortion, because these two have different effect on human observer.

This is achieved by computing the average correlation coefficient between reference image and error image. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation.