Ceed - A Database for Image Contrast Enhancement Evaluation

Beghdadi, Azeddine; Qureshi, Muhammad Ali; Sdiri, Bilel; Deriche, Mohamed; Alaya-Cheikh, Faouzi

doi:10.1109/cvcs.2018.8496603

Cited by 10 publications

(7 citation statements)

References 19 publications

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“…There are a limited number of datasets on image enhancement [1]. Contrast Enhancement Evaluation Database (CEED) [8] was created for testing different contrast enhancement techniques. Similar to the CEED, Colourlab Contrast Enhanced Image Dataset (CCEID) [1] focused on contrast enhanced images.…”

Section: Related Workmentioning

confidence: 99%

Image enhancement dataset for evaluation of image quality metrics

Altynay¹,

Marius²,

Borjali³

et al. 2022

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Image enhancement is important in different application areas such as medical imaging, computer graphics, and military applications. In this paper, we introduce a dataset with enhanced images. The images have been enhanced by five end users, and these have been evaluated by observers in an online image quality experiment. The enhancement steps by the end users and subjective results are analysed in detail. Furthermore, 38 image quality metrics have been evaluated on the introduced dataset to reveal their suitability to measure image enhancement. The results show that the image quality metrics have low to average performance on the new dataset.

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Section: Related Workmentioning

confidence: 99%

Image enhancement dataset for evaluation of image quality metrics

Altynay¹,

Marius²,

Borjali³

et al. 2022

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“…To select the reference images in our dataset we focused on complementing the already available datasets in the field of image contrast enhancement. Keeping this goal in mind, the reference images in the SEID dataset were selected from already available images in the CEED [11] (18 images) and CCEID [12] (12 images) datasets. This image selection allowed us to guarantee the diversity in the dataset with regards to visual contents and colorfulness in the images.…”

Section: Seid Datasetmentioning

confidence: 99%

“…The Contrast Enhancement Evaluation Dataset (CEED) [11] includes 30 references and 180 enhanced images (six different enhanced image per reference image). For image enhancement, six different contrast enhancement techniques were applied.…”

Section: Introduction and Related Workmentioning

confidence: 99%

How Good is Too Good? A Subjective Study on Over Enhancement of Images

Azimian¹,

Azar²,

Amirshahi³

2021

cic

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For a long time different studies have focused on introducing new image enhancement techniques. While these techniques show a good performance and are able to increase the quality of images, little attention has been paid to how and when overenhancement occurs in the image. This could possibly be linked to the fact that current image quality metrics are not able to accurately evaluate the quality of enhanced images. In this study we introduce the Subjective Enhanced Image Dataset (SEID) in which 15 observers are asked to enhance the quality of 30 reference images which are shown to them once at a low and another time at a high contrast. Observers were instructed to enhance the quality of the images to the point that any more enhancement will result in a drop in the image quality. Results show that there is an agreement between observers on when over-enhancement occurs and this point is closely similar no matter if the high contrast or the low contrast image is enhanced.

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“…Given the multitude of CE methods developed, it becomes practically difficult to objectively select the best method for a given application. In recent studies, some objective metrics based on the notion of contrast have been evaluated on dedicated databases for CEE [97], [138], [139], which is shown to be a challenging task.…”

Section: A Image Quality Assessment (Iqa)mentioning

confidence: 99%

A Critical Analysis on Perceptual Contrast and Its Use in Visual Information Analysis and Processing

Beghdadi¹,

Qureshi

Amirshahi

et al. 2020

IEEE Access

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Contrast in visible images is one of the most relevant characteristics of visual signals. Since the pioneering works performed in vision psychology and optics, different definitions have been proposed in the literature. However, for the time being there exist no definition of contrast on which the vision research and visual processing scientific community can agree on. This makes it critical to have a clear view on the notion of contrast and its use in various applications. One issue to consider is how to define and particularly use this important measure in developing image processing and analysis methods. In this paper, we present a critical review of contrast measures and associated models developed by the scientific community in vision, optics, and image processing. We also provide learned lessons and guidelines on the use of appropriate contrast measures in selected visual information processing and analysis applications. We discuss challenges and propose research avenues for models enriched by recent findings in the field of human vision research and machine learning. We believe that this work serves as a guideline and can potentially open new research perspectives to the scientific community working on visual information processing and analysis.

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Ceed - A Database for Image Contrast Enhancement Evaluation

Cited by 10 publications

References 19 publications

Image enhancement dataset for evaluation of image quality metrics

Image enhancement dataset for evaluation of image quality metrics

How Good is Too Good? A Subjective Study on Over Enhancement of Images

A Critical Analysis on Perceptual Contrast and Its Use in Visual Information Analysis and Processing

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