An essential determinant of the value of surrogate digital images is their quality. Image quality measurement has become crucial for most image processing applications. Over the past years , there have been many attempts to develop models or metrics for image quality that incorporate elements of human visual sensitivity. However, there is no current standard and objective definition of spectral image quality. This paper proposes a reliable automatic method for objective image quality measurement by wavelet analysis throughout the spatial frequency range. This is done by a detailed analysis of an image for a wide range of spatial frequency content, using a combination of modulation transfer function (MTF), brightness, contrast, saturation, sharpness and noise, as a more revealing metric for quality evaluation. A fast lifting wavelet algorithm is developed for computationally efficient spatial frequency analysis, where fine image detail corresponding to high spatial frequencies and image sharpness in regard to lower and mid -range spatial frequencies can be examined and compared accordingly. The wavelet frequency deconstruction is actually to extract the feature of edges in sub-band images. The technique provides a means to relate the quality of an image to the interpretation and quantification throughout the frequency range, in which the noise level is estimated in assisting with quality analysis. The experimental results of using this method for image quality measurement exhibit good correlation to subjective visual quality assessments.