Multiscale Image Quality Estimation

Multimedia communication is becoming pervasive because of the progress in wireless communications and multimedia coding. Estimating the quality of the visual content accurately is crucial in providing satisfactory service. State of the art visual quality assessment approaches are effective when the input image and the reference image have the same resolution. However, finding the quality of an image that has spatial resolution different than that of the reference image is still a challenging problem. To solve this problem, we develop a quality estimator (QE) which computes the quality of the input image without resampling the reference or the input images. In this work, we begin by identifying the potential weaknesses of previous approaches used to estimate the quality of experience. Next, we design a QE to estimate the quality of a distorted image with a lower resolution compared to the reference image. We also propose a subjective test environment to explore the success of the proposed algorithm in comparison with other QEs. When the input and test images have different resolutions, the subjective tests demonstrate that in most cases the proposed method works better than other approaches. In addition, the proposed algorithm also performs well when the reference image and the test image have the same resolution.