One of the most reliable ways of measuring visual image quality is through subjective experiments. However, as subjective evaluations are expensive, time consuming, and impractical in most situations, objective quality evaluation is used as an alternative. This paper presents a Full-Reference (FR) Wavelet based Image Quality Assessment algorithm (WIQA). The proposed metric evaluates the image quality in terms of saliency, sharpness, and blurriness. To this end, a novel compressive sampling based visual saliency prediction model along with an existing zero-crossing based edge detection method are combined to measure the image quality. Extensive performance evaluations indicate that incorporating visual saliency information improves the performance of the quality assessment task, and results in a competitive overall performance in comparison to the state-of-the-art metrics to assess JPEG, JPEG2K, and blur distortions.

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