Image quality assessment means estimating the quality of an image and it is used for many image processing applications. Image quality can be measured in two ways, subjective and objective method. Objective method is more preferable than subjective because most of the time the original image is not available for the comparison and it is not that much expensive like the subjective method. These ...

With the growing demand for geospatial data, the aerial imagery with high spatial, spectral, and temporal resolution achieves great development. It is imperative to evaluate whether the acquired images are qualified enough, since the further image mosaic asks for strict time consistency and a re-flight involves considerable resources. In this paper, we address the problem of quick aerial image ...

In this paper, a novel saliency theory based RR-IQA metric is introduced. As the human visual system is sensitive to the salient region, evaluating the image quality based on the salient region could increase the accuracy of the algorithm. In order to extract the salient regions, we use blob decomposition (BD) tool as a texture component descriptor. A new method for blob decomposition is propos...

The assessment of image compression result can not only evaluate the quality of image compression results and to a certain extent, can also find the advantages and drawbacks of various compression methods. At the same time, it can provide a reference for the compressed image restoration. Firstly, the classification and shortages of image quality assessment methods are presented. Then, several o...

Image quality measurement is very important for various image processing applications such as recognition, retrieval, classification, compression, restoration and similar fields. The images may contain different types of distortions like blur, noise, contrast change etc. So it is essential to rate the image quality appropriately. Traditionally subjective rating methods are used to evaluate the ...

Image Quality Assessment plays an important role in various image processing applications. It is still an active area of research. A great deal of effort has been made in recent years to develop objective image quality metrics that correlate well with perceived human quality measurement or subjective methods. Image quality assessment means estimating the quality of an image and it is used for m...

Assessment for image quality traditionally needs its original image as a reference. The conventional method for assessment like Mean Square Error (MSE) or Peak Signal to Noise Ratio (PSNR) is invalid when there is no reference. In this paper, we present a new No-Reference (NR) assessment of image quality using blur and noise. The recent camera applications provide high quality images by help of...

The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images in a perceptually consistent manner. Image QA algorithms generally interpret image quality as fidelity or similarity with a “reference” or “perfect” image in some perceptual space. In order to improve the assessment accuracy of white noise, Gauss blur, JPEG2000 compression an...