11 research outputs found
Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment
In the current world, user experience in various platforms matters a lot for different organizations. But providing a better experience can be challenging if the multimedia content on online platforms is having different kinds of distortions which impact the overall experience of the user. There can be various reasons behind distortions such as compression or minimal lighting condition while taking photos. In this work, a deep CNN-based Non-Screen Content and Screen Content NR-IQA framework is proposed which solves this issue in a more effective way. The framework is known as DNSSCIQ. Two different architectures are proposed based upon the input image type whether the input is a screen content or non-screen content image. This work attempts to solve this by evaluating the quality of such image
Comparative analysis of universal methods no reference quality assessment of digital images
The main purpose of this article is to conduct a comparative study of two well-known no-reference image
quality assessment algorithms BRISQUE and NIQE in order to analyze the relationship between subjective
and quantitative assessments of image quality. As experimental data, we used images with artificially
created distortions and mean expert assessments of their quality from the public databases TID2013, CISQ
and LIVE. Image quality scores were calculated using the NIQE, BRISQUE functions and their average.
The correlation coefficients of Pearson, Spearman and Kendall were analyzed between expert visual
assessments and quantitative scores of the image quality, as well as between the values of three compared
indicators. For the experiments, the Matlab system and values of its functions niqe and brisque normalized
to the range [0, 1] were used. The computation time of niqe is slightly less. The investigated functions
poorly estimate the contrast of images, but the additive Gaussian noise, Gaussian blur and loss in
compression by the JPEG2000 algorithm are better. The BRISQUE measure shows slightly better results
when evaluating images with additive Gaussian noise, while NIQE for blurred by Gaussian. The average of
the normalized values of NIQE and BRISQUE is a good compromise. The results of this work may be of
interest for the practical implementations of digital image analysis
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Reduced reference image and video quality assessments: review of methods
With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications
Reduced reference image and video quality assessments: review of methods
With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications