Results 61 to 70 of about 1,058,511 (183)
DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment
Sensors, 2020 Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning
Hayat Ullah +3 more
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Deep Learning frameworks for Image Quality Assessment [PDF]
, 2018 Technology is advancing by the arrival of deep learning and it finds huge application in image processing also. Deep learning itself sufficient to perform over all the statistical methods.
Channappayya, Sumohana, R, Aparna
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A Reduced-Reference Metric Based on the Interest Points in Color Images
, 2010 28th Picture Coding Symposium (PCS 2010)In the last decade, an important research effort has been dedicated to quality assessment from the subjective and the objective points of view.
Fernandez-Maloigne, Christine +2 more
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Image blur estimation based on the average cone of ratio in the wavelet domain [PDF]
, 2009 In this paper, we propose a new algorithm for objective blur estimation using wavelet decomposition. The central idea of our method is to estimate blur as a function of the center of gravity of the average cone ratio (ACR) histogram.
Philips, Wilfried +3 more
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Image Analysis and Stereology, 2018 Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur
Kanjar De, Masilamani V
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Blind Stereoscopic Image Quality Assessment Based on Hierarchical Learning
IEEE Access, 2019 We proposed a blind image quality assessment model which used classification and prediction for three-dimensional (3D) image quality assessment (denoted as CAP-3DIQA) that can automatically evaluate the quality of stereoscopic images.
Tsung-Jung Liu +3 more
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No-Reference Image Quality Assessment through SIFT Intensity [PDF]
Applied Mathematics & Information Sciences, 2014 SIFT (Scale Invariant Feature Transform) points are scale-space extreme points, representing local minutiae structure features in the Gaussian scale space. SIFT intensity, as a novel no-ref erence metric, is feasible to assess various common distortions without the access to reference images.
Tongfeng Sun, Shifei Ding, Xinzheng Xu
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Blind Image Quality Assessment for Super Resolution via Optimal Feature Selection
IEEE Access, 2020 Methods for image Super Resolution (SR) have started to benefit from the development of perceptual quality predictors that are designed for super resolved images.
Juan Beron +2 more
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Why Are Deep Representations Good Perceptual Quality Features?
, 2020 Recently, intermediate feature maps of pre-trained convolutional neural networks have shown significant perceptual quality improvements, when they are used in the loss function for training new networks.
C Yang +14 more
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Full Reference Objective Quality Assessment for Reconstructed Background Images
, 2018 With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a ...
Karam, Lina, Shrotre, Aditee
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