Objective image quality evaluation plays an important role in other image and video processing techniques, such as compression, interpolation, noise reduction, where each evaluation method is based on image quality estimation. The thesis presents innovative approach to the objective quality evaluation that could be computed using multiresolution image analysis. It is based on DWT subband decomposition or SPWT decomposition, which properties are similar to human visual system (HVS) characteristics facilitating integration of SPWT into image quality evaluation. SPWT decomposition is done with multiresolution analysis of a signal that allows us to decompose a signal into approximation, bandpass and highpass subbands. SPWT coefficients are computed using wavelet filter banks with 2 orientations (vertical and horizontal) in spatial domain. Wavelet coefficients are used to compute new image quality measure (IQM). IQM is defined as a product of a mean contrast and structure between absolute bandpass coefficients of original and degraded image. IQM measure is compared with 12 other existing image quality measures using new image database VCL@FER and 6 other image databases. Correlation between objective and subjective image quality measures is presented, for all image databases. Statistical significance between variances of residuum (between linearized objective and subjective measures) is also presented. Results show that IQM measure yields best correlation results with subjective grades and that it is not dependent on image content and degradation type.