No reference image quality assessment for JPEG2000 based on spatial features

Sazzad, Z. M. Parvez; Kawayoke, Yoshikazu; Horita, Yuukou

doi:10.1016/j.image.2008.03.005

Cited by 108 publications

(20 citation statements)

References 16 publications

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“…[12][13][14] These approaches extract distortion-specific features which are related to image quality, such as edge strength at block boundaries. The first one is distortion specific, which can predict subjective quality scores when the distortion type is preknown.…”

Section: Previous Workmentioning

confidence: 99%

No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

Jia

Sun

et al. 2014

Opt. Eng

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The goal of no-reference/blind image quality assessment (NR-IQA) is to devise a perceptual model that can accurately predict the quality of a distorted image as human opinions, in which feature extraction is an important issue. However, the features used in the state-of-the-art "general purpose" NR-IQA algorithms are usually natural scene statistics (NSS) based or are perceptually relevant; therefore, the performance of these models is limited. To further improve the performance of NR-IQA, we propose a general purpose NR-IQA algorithm which combines NSS-based features with perceptually relevant features. The new method extracts features in both the spatial and gradient domains. In the spatial domain, we extract the point-wise statistics for single pixel values which are characterized by a generalized Gaussian distribution model to form the underlying features. In the gradient domain, statistical features based on neighboring gradient magnitude similarity are extracted. Then a mapping is learned to predict quality scores using a support vector regression. The experimental results on the benchmark image databases demonstrate that the proposed algorithm correlates highly with human judgments of quality and leads to significant performance improvements over state-of-the-art methods. Downloaded From: http://opticalengineering.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms

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Section: Previous Workmentioning

confidence: 99%

No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

Jia

Sun

et al. 2014

Opt. Eng

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“…The second approach is well suited for assessing the quality of images distorted by known distortions. The algorithms of this approach quantify one or more distortions such as blockiness [20,27], blur [22,2] or ringing [9,10] and score the image accordingly. The third and last approach is a general-purpose method.…”

Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment Based on the Use of Saliency Maps and a Multivariate Gaussian Distribution

Charrier¹,

Saadane²,

Fernández-Maloigne³

2019

Lecture Notes in Computer Science

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To cite this version:Christophe Charrier, Abdelhakim Saadane, Christine Fernandez-Maloigne. Blind Image Quality Assessment based on the use of Saliency Maps and a Multivariate Gaussian Distribution.Abstract. With the widespread use of image processing technologies, objective image quality metrics are a fundamental and challenging problem. In this paper, we present a new No-Reference Image Quality Assessment (NR-IQA) algorithm based on visual attention modeling and a multivariate Gaussian distribution to predict the final quality score from the extracted features. Computational modeling of visual attention is performed to compute saliency maps at three resolution levels. At each level, distortions of the input image are extracted and weighted by the saliency maps in order to highlight degradations of visually attracting regions. The generated features are used by a probabilistic model to predict the final quality score. Experimental results demonstrate the effectiveness of the metric and show better performance when compared to well known NR-IQA algorithms.

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“…Although a number of new objective IQMs have been developed in the past few decades, the majority of these require the original undistorted image as a reference [17]. The development of objective NR IQMs is a relatively new topic in the field of image processing, and, in more recent years, a large number of NR metrics have been proposed for evaluating image quality.…”

Section: Introductionmentioning

confidence: 99%

Statistical Evaluation of No-Reference Image Quality Assessment Metrics for Remote Sensing Images

Yang

2017

IJGI

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Image quality assessment plays an important role in image processing applications. In many image applications, e.g., image denoising, deblurring, and fusion, a reference image is rarely available for comparison with the enhanced image. Thus, the quality of enhanced images must be evaluated blindly without references. In recent years, many no-reference image quality metrics (IQMs) have been proposed for assessing digital image quality. In this paper, we first review 21 commonly employed no-reference IQMs. Second, we apply these measures to Quickbird images with three different types of general content (urban, rural, and harbor) subjected to three types of degradation (average filtering, Gaussian white noise, and linear motion degradation), each with 40 degradation levels. We evaluate the robustness of the IQMs based on the criteria of prediction accuracy, prediction monotonicity, and prediction consistency. Then, we perform factor analysis on those IQMs deemed robust, and cluster them into several components. We then select the IQM with the highest loading coefficient as the representative IQM for that component. Experimental results suggest that different measures perform differently for images with different contents and subjected to different types of degradation. Generally, the degradation method has a stronger effect than the image content on the evaluation results of an IQM. The same IQM can provide opposite dependences on the level of degradation for different degradation types, and an IQM that performed well with one type of degradation may not perform well with another type. The training-based measures are not appropriate for remote sensing images because the results are highly dependent on the samples employed for training. Only seven of the 21 IQMs were found to fulfill the requirements of robustness. Edge intensity (EI) and just noticeable distortion (JND) are suggested for evaluating the quality of images subjected to average filter degradation. EI, blind image quality assessment through anisotropy (BIQAA), and mean metric (MM) are suggested for evaluating the quality of images subjected to Gaussian white noise degradation. Laplacian derivative (LD), JND, and standard deviation (SD) are suggested for evaluating the quality of images subjected to linear motion. Finally, EI is suggested for evaluating the quality of an image subjected to an unknown type of degradation.

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No reference image quality assessment for JPEG2000 based on spatial features

Cited by 108 publications

References 16 publications

No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

Blind Image Quality Assessment Based on the Use of Saliency Maps and a Multivariate Gaussian Distribution

Statistical Evaluation of No-Reference Image Quality Assessment Metrics for Remote Sensing Images

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