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Dynamic Receptive Field Generation for Full-Reference Image Quality Assessment
IEEE Transactions on Image Processing, 2020Most full-reference image quality assessment (FR-IQA) methods advanced to date have been holistically designed without regard to the type of distortion impairing the image. However, the perception of distortion depends nonlinearly on the distortion type.
Woojae Kim +3 more
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A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing, 2006Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments.
Hamid Rahim, Sheikh +2 more
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Graph-Represented Distribution Similarity Index for Full-Reference Image Quality Assessment
IEEE Transactions on Image ProcessingIn this paper, we propose a graph-represented image distribution similarity (GRIDS) index for full-reference (FR) image quality assessment (IQA), which can measure the perceptual distance between distorted and reference images by assessing the disparities between their distribution patterns under a graph-based representation.
Wenhao Shen +4 more
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Full reference quality assessment of downsized images
2017 International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), 2017Resizing image processing tools are in vogue nowadays due to various practical reasons. The images are resized into different resolutions and scales. The quality of the image may get affected by resizing the original image. In this paper, quality of downsized images is evaluated.
Mohammad Usman Khan +2 more
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Neural network-based full-reference image quality assessment
2016 Picture Coding Symposium (PCS), 2016This paper presents a full-reference (FR) image quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN extracts features from distorted and reference image patches and estimates the perceived quality of the distorted ones by combining and regressing the feature vectors using two fully connected layers.
Sebastian Bosse +4 more
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Sampled efficient full-reference image quality assessment models
2016 50th Asilomar Conference on Signals, Systems and Computers, 2016Existing Ml-reference image quality assessment models first compute a full image quality-predictive feature map followed by a spatial pooling scheme, thereby producing a single quality score. Here we study spatial sampling strategies that can be used to more efficiently compute reliable picture quality scores.
Christos G. Bampis +2 more
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Machine learning to design full-reference image quality assessment algorithm
Signal Processing: Image Communication, 2012A crucial step in image compression is the evaluation of its performance, and more precisely, available ways to measure the quality of compressed images. In this paper, a machine learning expert, providing a quality score is proposed. This quality measure is based on a learned classification process in order to respect human observers.
Charrier, Christophe +2 more
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Image quality assessment by preprocessing and full reference model combination
SPIE Proceedings, 2009This paper focuses on full-reference image quality assessment and presents different computational strategies aimed to improve the robustness and accuracy of some well known and widely used state of the art models, namely the Structural Similarity approach (SSIM) by Wang and Bovik and the S-CIELAB spatial-color model by Zhang and Wandell.
SCHETTINI, RAIMONDO +3 more
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Full Reference Image Quality Assessment: Limitation
2014 22nd International Conference on Pattern Recognition, 2014In this work, we propose to study the universality of the Full-Reference Image Quality metrics (FR-IQMs) and show the no-relevance to use this kind of metrics without considering the degradation type contained in the image. Different experimental tests have been done in order to analyze its performance.
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