SCID: full reference spatial color image quality metric

Ouni, Sonia; Chambah, Majed; Herbin, Michel; Zagrouba, Ezzeddine

doi:10.1117/12.806031

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…2) CD measures for natural images: S-CIELAB [57], Hong06 [18], Ouni08 [39], Jaramillo19 [38] and CD-Net [51], 3) full-reference image quality model: SSIM [49], FLIP [3], PieAPP [44], LPIPS [56] and DISTS [10], and 4) JND methods: Chou07 [8] and Butteraugli [1]. We employ official implementations of Butteraugli and FLIP, and retrain PieAPP, LPIPS, and DISTS on the same training set as CD-Flow.…”

Section: Resultsmentioning

confidence: 99%

“…To incorporate spatial context into CD assessment, Zhang and Wandell [57] presented S-CIELAB, which extends CIELAB by adding spatial low-pass filtering as preprocessing. Similarly, Ouni et al [39] provided a spatial extension of CIEDE2000. Lee et al [26] re-examined histogram intersection, which is widely used in color image index, for the purpose of color image similarity assessment.…”

Section: Related Workmentioning

confidence: 99%

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Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

Chen

Alturas

2023

Preprint

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This work presents a case study on The University of Hong Kong-Shenzhen Hospital (HKU-SZH), which was the first to implement an outpatient appointment registration system. This study provides an anatomy of the hospital outpatient process through various methods and theories, such as literature review, field research, expert consultation, business process improvement theory and Information technology, with the objective of identifying objectives and strategies of the hospital case in improving its outpatient process. By means of outpatient process improvement, this study aim to increase the case hospital’s efficiency and raise its patients’ satisfaction so that the hospital may enhance its comprehensive competence. In addition, an effective and operable methodology will be generated, which is expected to serve as a reference for other hospitals to improve their operation and their management. Therefore, the research question is to know which factors most influence patient satisfaction. It was found that service attitude, service value, and waiting time have a significant influence on patient satisfaction.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

Chen

Alturas

2023

Preprint

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“…Ouni et al 90 present a full reference color metric called spatial color image difference (SCID), which is perceptually correlated with the HVS. In this work, ACE algorithm is used when a reference image is missing; thus, the color difference is computed between the target image and the same image enhanced/restored through ACE.…”

Section: Image Qualitymentioning

confidence: 99%

“…authors combine a region-based segmentation with ACE algorithm, to segment fish in images with a complex background in water. A new full-reference image quality metric, named SCID is presented by Ouni et al in Ref 90…”

mentioning

confidence: 99%

Scoping review on automatic color equalization algorithm

2021

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Digital image processing is at the base of everyday applications aiding humans in several fields, such as underwater monitoring, analysis of cultural heritage drawings, and medical imaging for computer-aided diagnosis. The starting point of all such application regards the image enhancement step. A desirable image enhancement step should simultaneously standardize the illumination in the image set, possibly removing bad or not-uniform illumination effects, and reveal all hidden details. In 2002, a successful perceptual image enhancement model, the automatic color equalization (ACE) algorithm, was proposed, which mimics the color and contrast adjustment of the human visual system (HVS). Given its widespread usage, its correlation with the HVS, and since it is easily implementable, we propose a scoping review to identify and classify the available evidence on ACE, starting from the papers citing the two funding papers on the algorithm. The aim of this work is the identification of what extent and in which ways ACE may have influenced the research in the color imaging field. Thanks to an accurate process of papers tagging, classification, and validation, we provide an overview of the main application domains in which ACE was successfully used and of the different ways in which this algorithm was implemented, modified, used, or compared.

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“…The objective image quality assessment metrics can be classified into two categories. The first is based on mathematical measures like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Signal to Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR) and Spatial Color Image Quality Metric (SCID) [6]. The second is based on characteristics of the Human Visual System (HVS) like mean structural similarity index (MSSIM) [7].…”

Section: Introductionmentioning

confidence: 99%

A New No-reference Method for Color Image Quality Assessment

Ouni¹,

Zagrouba²,

Chambah³

2012

IJCA

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Image quality assessment (IQA) is a complex problem due to subjective nature of human visual perception. Human have always seen the world in color. The widely objective metrics used are mean squared error (MSE), peak signal to noise ratio (PSNR), and human visual system based on structural similarity and edge based similarity. The problem of these objective metrics that they evaluate the quality of grayscale images only and don't make use of image color information. Also, we must have the presence of original image. Unfortunately, the field of no-reference (NR) color IQA has been largely unexplored although the color is a powerful descriptor that often simplifies the object identification and extraction from a scene so color information also could influence human beings' judgments. So, in this paper a new no reference methods for color IQA are proposed. These methods are based on different statistical analyses and easy to calculate and applicable to various image processing. This proposed metrics are mathematically defined and overcame the limitations of existing metrics to assess the quality of the color in the image. The experiment results on various image distortion show that our proposed no reference metrics have a comparable performance to the other traditional error summation metrics and to the leading metrics available in literature.

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SCID: full reference spatial color image quality metric

Cited by 5 publications

References 7 publications

Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

Scoping review on automatic color equalization algorithm

A New No-reference Method for Color Image Quality Assessment

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