An Improved SPSIM Index for Image Quality Assessment

Frackiewicz, Mariusz; Szolc, Grzegorz; Palus, Henryk

doi:10.20944/preprints202102.0189.v1

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…The mean values of the intensity and chrominance components are extracted within each superpixel and compared between a reference and target image to describe local similarity index precisely. Frackiewicz et al improved SPSIM and developed an improved SPSIM index for IQA [24]. Their method revises SPSIM in two ways: new color space replaces the YUV space and exploits the calculation method from Mean Deviation Similarity Index (MDSI) index [25].…”

Section: Introductionmentioning

confidence: 99%

Deep Superpixel-Based Network For Blind Image Quality Assessment

Yang

Wang

2021

Preprint

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The goal in a blind image quality assessment (BIQA) method is to simulate the process of evaluating images by human eyes and accurately assess the quality of the image. Although many methods effectively identify degradation, they do not fully consider the semantic content in images resulting in distortion. In order to fill this gap, we propose a deep adaptive superpixel-based network, namely DSN-IQA, to assess the quality of image based on multi-scale and superpixel segmentation. The DSN-IQA can adaptively accept arbitrary scale images as input images, making the assessment process similar to human perception. The network uses two models to extract multi-scale semantic features and generate a superpixel adjacency map. These two elements are united together via feature fusion to accurately predict image quality. Experimental results on different benchmark databases demonstrate that our method is highly competitive with other methods when assessing challenging authentic image databases. Also, due to adaptive deep superpixel-based network, our method accurately assesses images with complicated distortion, much like the human eye.

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Section: Introductionmentioning

confidence: 99%

Deep Superpixel-Based Network For Blind Image Quality Assessment

Yang

Wang

2021

Preprint

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Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics

2022

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Magnetic resonance imaging (MRI) is an information-rich research tool used in diagnostics using image processing applications (IPAs), and the results are utilized in machine learning. Therefore, testing of IPAs for credible results is vital. A deficient IPA would cause the related taxonomies of the machine learning to be defective as well and diagnosis will not be perfect. Accurate disease detection by IPA, without surgical intervention, leads to improved quality of treatment. Current challenges for testing of IPA include an absence of a test oracle. One way to alleviate the test oracle problem is metamorphic testing which identifies the specific properties called metamorphic relations of the system under test. Previously metamorphic testing approaches have been applied and evaluated on IPAs, but there is no previous work on evaluation of metamorphic testing on MRI images. In this work, we have evaluated effectiveness of metamorphic testing on edge detection of MRI images. The aim of this study is to determine which metamorphic relations are more effective for metamorphic testing of edge detection in MRI images such as T1, T2 and flair images. Our results show that the fault detection rate of MR4 is highest and MR2 is the lowest among all type of MRI images at the threshold of 0.95.

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Full-Reference Image Quality Assessment Based on Grünwald–Letnikov Derivative, Image Gradients, and Visual Saliency

Varga

2022

Electronics

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The purpose of image quality assessment is to estimate digital images’ perceptual quality coherent with human judgement. Over the years, many structural features have been utilized or proposed to quantify the degradation of an image in the presence of various noise types. Image gradient is an obvious and very popular tool in the literature to quantify these changes in the images. However, gradient is able to characterize images locally. On the other hand, results from previous studies indicate that global contents of a scene are analyzed before the local features by the human visual system. Relying on these features of the human visual system, we propose a full-reference image quality assessment metric that characterizes the global changes of an image by the Grünwald–Letnikov derivatives and the local changes by image gradients. Moreover, visual saliency is also utilized for weighting the changes in the images and emphasizing those areas of the image which are salient to the human visual system. To prove the efficiency of the proposed method, massive experiments were carried out on publicly available benchmark image quality assessment databases.

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An Improved SPSIM Index for Image Quality Assessment

Cited by 6 publications

References 15 publications

Deep Superpixel-Based Network For Blind Image Quality Assessment

Deep Superpixel-Based Network For Blind Image Quality Assessment

Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics

Full-Reference Image Quality Assessment Based on Grünwald–Letnikov Derivative, Image Gradients, and Visual Saliency

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