Blind image quality evaluation using perception based features

Venkatanath, N; Praneeth, D; Bh, Maruthi Chandrasekhar; Channappayya, Sumohana S.; Medasani, S.

doi:10.1109/ncc.2015.7084843

Cited by 570 publications

(320 citation statements)

References 15 publications

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“…Signal to noise ratio (SNR) and the no‐reference perception‐based image quality evaluator (PIQUE) of every sampling pattern were assessed on the QA head SNR phantom (Model: 2321556, General Electric, GE, Milwaukee, WI). 3D‐FSE and 3D‐GRASE phantom images for each sampling pattern were acquired following the in vivo protocols showed in Tables and for PD‐weighted and

T_{2}

‐weighted images, respectively.…”

Section: Methodsmentioning

confidence: 99%

Compressed Sensing 3D‐GRASE for faster High‐Resolution MRI

Cristobal-Huerta

Poot

Vogel³

et al. 2019

Magnetic Resonance in Med

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Purpose High‐resolution three‐dimensional (3D) structural MRI is useful for delineating complex or small structures of the body. However, it requires long acquisition times and high SAR, limiting its clinical use. The purpose of this work is to accelerate the acquisition of high‐resolution images by combining compressed sensing and parallel imaging (CSPI) on a 3D‐GRASE sequence and to compare it with a (CS)PI 3D‐FSE sequence. Several sampling patterns were investigated to assess their influence on image quality. Methods The proposed k‐space sampling patterns are based on two undersampled k‐space grids, variable density (VD) Poisson‐disc, and VD pseudo‐random Gaussian, and five different trajectories described in the literature. Bloch simulations are performed to obtain the transform point spread function and evaluate the coherence of each sampling pattern. Image resolution was assessed by the full‐width at half‐maximum (FWHM). Prospective CSPI 3D‐GRASE phantom and in vivo experiments in knee and brain are carried out to assess image quality, SNR, SAR, and acquisition time compared to PI 3D‐GRASE, PI 3D‐FSE, and CSPI 3D‐FSE acquisitions. Results Sampling patterns with VD Poisson‐disc obtain the lowest coherence for both PD‐weighted and T2‐weighted acquisitions. VD pseudo‐random Gaussian obtains lower FWHM, but higher sidelobes than VD Poisson‐disc. CSPI 3D‐GRASE reduces acquisition time (43% for PD‐weighted and 40% for T2‐weighted) and SAR (∼45% for PD‐weighted and T2‐weighted) compared to CSPI 3D‐FSE. Conclusions CSPI 3D‐GRASE reduces acquisition time compared to a CSPI 3DFSE acquisition, preserving image quality. The design of the sampling pattern is crucial for image quality in CSPI 3D‐GRASE image acquisitions.

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T_{2}

‐weighted images, respectively.…”

Section: Methodsmentioning

confidence: 99%

Compressed Sensing 3D‐GRASE for faster High‐Resolution MRI

Cristobal-Huerta

Poot

Vogel³

et al. 2019

Magnetic Resonance in Med

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show abstract

“…Each feature is classified according to the degree of distortion and assigned a score. The score range is between 0 to 100 and lower values represent better quality [22]. From the overall assessment based on the qualitative comparisons, we confirm that the proposed method produces reasonable detail enhancement in the dark region and good HDR rendering results.…”

Section: Simulationsmentioning

confidence: 55%

Contrast Sensitivity Based Multiscale Base–Detail Separation for Enhanced HDR Imaging

Kwon

Lee

2020

Applied Sciences

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High dynamic range (HDR) imaging is used to represent scenes with a greater dynamic range of luminance on a standard dynamic range display. Usually, HDR images are synthesized through base–detail separations. The base layer is used for tone compression and the detail layer is used for detail preservation. The representative detail-preserved algorithm iCAM06 has a tendency to reduce the sharpness of dim surround images, because of the fixed edge-stopping function of the fast-bilateral filter (FBF). This paper proposes a novel base–detail separation and detail compensation technique using the contrast sensitivity function (CSF) in the segmented frequency domain. Experimental results show that the proposed rendering method has better sharpness features and image quality than previous methods correlated by the human visual system.

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“…To compare our proposed method to other state-of-the-art algorithms, we collected seven traditional learning-based NR-IQA metrics (BIQI [45], BLIINDS-II [3], BRISQUE [6], CORNIA [46], DIIVINE [2], HOSA [47], and SSEQ [4]), four deep learning based methods (BosICIP [15], CNN [14], DIQaM-NR [48], and WaDIQaM-NR [48]), and one opinion-unaware method (PIQE [49]) whose original source code are available. Moreover, we reimplemented the deep learning based DeepBIQ [18] method.…”

Section: Comparison To the State-of-the-artmentioning

confidence: 99%

Multi-Pooled Inception Features for No-Reference Image Quality Assessment

Varga

2020

Applied Sciences

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Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal—called MultiGAP-NRIQA—is able to outperform the state-of-the-art on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.

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Blind image quality evaluation using perception based features

Cited by 570 publications

References 15 publications

Compressed Sensing 3D‐GRASE for faster High‐Resolution MRI

Compressed Sensing 3D‐GRASE for faster High‐Resolution MRI

Contrast Sensitivity Based Multiscale Base–Detail Separation for Enhanced HDR Imaging

Multi-Pooled Inception Features for No-Reference Image Quality Assessment

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