The recent development of digital image acquisition technologies leads to better image quality, in terms of spatial resolution and sensitivity. Image quality is a characteristic of an image that measures the perceived image degradation. Several techniques and metrics are proposed which can be classified as Full-Reference (FR) method, No-Reference (NR) method and Reduced Reference (RR) method. I...

No reference image quality assessment (NR-IQA) has attracted great attention due to the increasing demand in developing perceptually friendly applications. The crucial challenge of this task is how to accurately measure the naturalness of an image. In this paper, we propose a novel parametric image representation which is derived from the generic image prior (GIP). More specifically, we utilize...

The evaluation of visual quality of color images has become very important and challenging task due to explosion of multimedia and graphics content on internet. An image exhibits loss in color information due to introduction of noise, blur, blocking artefacts, channel distortion and also during lossy compression. The primary goal of Image Quality Metric (IQM) is to measure emergence of such dis...

With the advent of various video compression standards and a proliferation of digital video coding products that are just beginning to appear in the marketplace, it has become increasingly important to devise image/video quality assessment algorithms that will standardize the assessment of compressed digital image/video quality. The subjective assessment of Mean Opinion Score (MOS) is very tedi...

Quantitative measures of image quality (IQ) are routinely obtained during the evaluation of imaging systems. These measures, however, do not necessarily correlate with the IQ of the actual clinical images, which can also be affected by factors such as patient positioning. No quantitative method currently exists to evaluate clinical IQ. Therefore, we investigated the potential of using computeri...

The digital image quality assessment is one of the most relevant aspects of contemporary digital image processing. A rapid development of some modern quality assessment techniques in recent years has caused the introduction of some new metrics, much better correlated with the Human Visual System (HVS) than the traditional ones such as the Mean Squared Error (MSE) or PSNR (Peak Signal to Noise R...

Conventionally, image quality assessment (IQA) algorithms represent image quality as linearity with a “reference” or “perfect” image. Obvious drawback of this method is that the many times original image may not be accessible for the QA algorithm. This paper proposes an image quality assessment of natural-scene statistic-based on DCT score prediction approach. It operates in transform domain. M...

Recent advances in high dynamic range (HDR) capture and display technologies have attracted a lot of interest from scientific, professional, and artistic communities. As in any technology, the evaluation of HDR systems in terms of quality of experience is essential. Subjective evaluations are time consuming and expensive, and thus objective quality assessment tools are needed as well. In this p...

As the need of quality assessment increases day by day. Here develops an efficient general-purpose blind image/video quality assessment (I/VQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The image/video quality assessment approach relies on a simple Bayesian inference model to predict image/video quality scores from certain extracted ...

Regarding the important constraints due to subjective quality assessment, objective image quality assessment has recently been extensively studied. Such metrics are usually of three kinds, they might be Full Reference (FR), Reduced Reference (RR) or No Reference (NR) metrics. We focus here on a new technique, which recently appeared in quality assessment context: data-hiding-based image quality...