Studying the effect of optimizing image quality in salient regions at the expense of background content

Manufacturers of commercial display devices continuously try to improve the perceived image quality of their products. By applying postprocessing techniques on the incoming signal, they aim to enhance the quality level perceived by the viewer. These postprocessing techniques are usually applied globally over the whole image but may cause side...

Studying the relative impact of ghosting and noise on the perceived quality of MR images

In current magnetic resonance (MR) imaging systems, design choices are confronted with a trade-off between structured (i.e. artifacts) and unstructured noise. The impact of both types of noise on perceived image quality, however, is so far unknown, while this knowledge would be highly beneficial for further improvement of MR imaging systems. In...

Interactions of visual attention and quality perception

Several attempts to integrate visual saliency information in quality metrics are described in literature, albeit with contradictory results. The way saliency is integrated in quality metrics should reflect the mechanisms underlying the interaction between image quality assessment and visual attention. This interaction is actually two-fold: (1)...

Efficient neural-network-based no-reference approach to an overall quality metric for JPEG and JPEG2000 compressed images

Reliably assessing overall quality of JPEG/JPEG2000 coded images without having the original image as a reference is still challenging, mainly due to our limited understanding of how humans combine the various perceived artifacts to an overall quality judgment. A known approach to avoid the explicit simulation of human assessment of overall...

Issues in the design of a no-reference metric for perceived blur

Developing an objective metric, which automatically quantifies perceived image quality degradation induced by blur, is highly beneficial for current digital imaging systems. In many applications, these objective metrics need to be of the noreference (NR) type, which implies that quality prediction is based on the distorted image only. Recent...

Issues in the design of a no-reference metric for perceived blur

Developing an objective metric, which automatically quantifies perceived image quality degradation induced by blur, is highly beneficial for current digital imaging systems. In many applications, these objective metrics need to be of the noreference (NR) type, which implies that quality prediction is based on the distorted image only. Recent...

Studying the effect of optimizing the image quality in saliency regions at the expense of background content

Manufacturers of commercial display devices continuously try to improve the perceived image quality of their products. By applying some post processing techniques on the incoming image signal, they aim to enhance the quality level perceived by the viewer. Applying such techniques may cause side effects on different portions of the processed...

Comparing subjective image quality measurement methods for the creation of public databases

A Perceptually Relevant No-Reference Blockiness Metric Based on Local Image Characteristics

A novel no-reference blockiness metric that provides a quantitative measure of blocking annoyance in block-based DCT coding is presented. The metric incorporates properties of the human visual system (HVS) to improve its reliability, while the additional cost introduced by the HVS is minimized to ensure its use for real-time processing. This is...