No-reference image quality assessment aims to predict the visual quality of distorted images without examining the original image as a reference. Most no-reference image quality metrics which have been already proposed are designed for one or a set of predefined specific distortion types and are unlikely to generalize for evaluating images degraded with other types of distortion. There is a str...

Image quality assessment (IQA) is an important task of image processing and has diverse applications, such as super-resolution reconstruction, transmission monitoring systems. This paper proposes a perceptual hashing algorithm with complementary color wavelet transform (CCWT) compressed sensing (CS) for reduced-reference (RR) IQA. The CCWT exploited to decompose input into different sub-bands. ...

Measurement of visual quality is of fundamental importance for numerous image and video processing applications. This paper presented a novel and concise reduced reference (RR) image quality assessment method. Statistics of local binary pattern (LBP) is introduced as a similarity measure to form a novel RR image quality assessment (IQA) method for the first time. With this method, first, the te...

User-oriented image quality assessment has become a key factor in multimedia communications as a means of monitoring perceptual service quality. However, existing image quality metrics such as Peak Signal-to-Noise Ratio (PSNR) are inappropriate for in-service quality monitoring since they require the original image to be available at the receiver. Although PSNR and others are objective metrics,...

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...

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...

Measuring visual quality of printed media is important as printed products play an essential role in every day life. The image quality assessment has been an active research topic in digital image processing, but adapting the developed methods to measuring visual quality of printed media has been considered rarely and is not straightforward. In this work, different methods for the quality asses...

This article proposes a real-time test and verification system for full-reference automatic image quality assessment and verification of digital TV sets. Digital camera is used for acquisition of the TV screen content in order to ensure quality assessment of the content as perceived by the user. Test has been executed in three steps: image acquisition by camera, TV screen content extraction and...

Measurement of image quality is crucial for many imageprocessing algorithms, such as acquisition, compression, restoration, enhancement and reproduction. Traditionally, researchers in image quality assessment have focused on equating image quality with similarity to a ‘reference’ or ‘perfect’ image. The field of blind, or no-reference, quality assessment, in which image quality is predicted wit...