Ms. Sonia Ouni Profile

Sonia Ouni

at Univ de Reims Champagne-Ardenne

SPIE Involvement:

Author

Proceedings Article | 19 January 2009 Paper

Toward an automatic subjective image quality assessment system

M. Chambah, S. Ouni, M. Herbin, E. Zagrouba

Proceedings Volume 7242, 72420E (2009) https://doi.org/10.1117/12.806004

KEYWORDS: Image quality, Neural networks, Image processing, Molybdenum, Quality systems, Feature extraction, Computing systems, Video, Systems modeling, Image compression

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Proceedings Article | 19 January 2009 Paper

SCID: full reference spatial color image quality metric

S. Ouni, M. Chambah, M. Herbin, E. Zagrouba

Proceedings Volume 7242, 72420U (2009) https://doi.org/10.1117/12.806031

KEYWORDS: Image quality, Color difference, Visual system, Distortion, Image compression, Visualization, Molybdenum, Image analysis, Image restoration, Image filtering

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Proceedings Article | 28 January 2008 Paper

Are existing procedures enough? Image and video quality assessment: review of subjective and objective metrics

Sonia Ouni, Majed Chambah, Michel Herbin, Ezzeddine Zagrouba

Proceedings Volume 6808, 68080Q (2008) https://doi.org/10.1117/12.760803

KEYWORDS: Image quality, Video, Image compression, Video compression, Visualization, Quality measurement, Image processing, Video processing, Visual process modeling, Image transmission

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Images and videos are subject to a wide variety of distortions during acquisition, digitizing, processing, restoration, compression, storage, transmission and reproduction, any of which may result in degradation in visual quality. That is why image quality assessment plays a major role in many image processing applications. Image and video quality metrics can be classified by using a number of criteria such as the type of the application domain, the predicted distortion (noise, blur, etc.) and the type of information needed to assess the quality (original image, distorted image, etc.). In the literature, the most reliable way of assessing the quality of an image or of a video is subjective evaluation [1], because human beings are the ultimate receivers in most applications. The subjective quality metric, obtained from a number of human observers, has been regarded for many years as the most reliable form of quality measurement. However, this approach is too cumbersome, slow and expensive for most applications [2]. So, in recent years a great effort has been made towards the development of quantitative measures. The objective quality evaluation is automated, done in real time and needs no user interaction. But ideally, such a quality assessment system would perceive and measure image or video impairments just like a human being [3]. The quality assessment is so important and is still an active and evolving research topic because it is a central issue in the design, implementation, and performance testing of all systems [4, 5]. Usually, the relevant literature and the related work present only a state of the art of metrics that are limited to a specific application domain. The major goal of this paper is to present a wider state of the art of the most used metrics in several application domains such as compression [6], restoration [7], etc. In this paper, we review the basic concepts and methods in subjective and objective image/video quality assessment research and we discuss their performances and drawbacks in each application domain. We show that if in some domains a lot of work has been done and several metrics were developed, on the other hand, in some other domains a lot of work has to be done and specific metrics need to be developed.

Proceedings Article | 28 January 2008 Paper

DAF: differential ACE filtering image quality assessment by automatic color equalization

S. Ouni, M. Chambah, C. Saint-Jean, A. Rizzi

Proceedings Volume 6808, 68080W (2008) https://doi.org/10.1117/12.760813

KEYWORDS: Image quality, Image filtering, Molybdenum, Colorimetry, Optical filters, Visualization, Quality measurement, Image enhancement, Quality systems, Video

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Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being. But in reality, objective quality metrics do not necessarily correlate well with perceived quality [1]. Plus, some measures assume that there exists a reference in the form of an "original" to compare to, which prevents their usage in digital restoration field, where often there is no reference to compare to. That is why subjective evaluation is the most used and most efficient approach up to now. But subjective assessment is expensive, time consuming and does not respond, hence, to the economic requirements [2,3]. Thus, reliable automatic methods for visual quality assessment are needed in the field of digital film restoration. The ACE method, for Automatic Color Equalization [4,6], is an algorithm for digital images unsupervised enhancement. It is based on a new computational approach that tries to model the perceptual response of our vision system merging the Gray World and White Patch equalization mechanisms in a global and local way. Like our vision system ACE is able to adapt to widely varying lighting conditions, and to extract visual information from the environment efficaciously. Moreover ACE can be run in an unsupervised manner. Hence it is very useful as a digital film restoration tool since no a priori information is available. In this paper we deepen the investigation of using the ACE algorithm as a basis for a reference free image quality evaluation. This new metric called DAF for Differential ACE Filtering [7] is an objective quality measure that can be used in several image restoration and image quality assessment systems. In this paper, we compare on different image databases, the results obtained with DAF and with some subjective image quality assessments (Mean Opinion Score MOS as measure of perceived image quality). We study also the correlation between objective measure and MOS. In our experiments, we have used for the first image test set "Single Stimulus Continuous Quality Scale" (SSCQS) method and in the second "Double Stimulus Continuous Quality Scale" (DSCQS) method. The users, which are non-experts, were asked to identify their preferred image (between original and ACE filtered images) according to contrast, naturalness, colorfulness, quality, chromatic diversity and overall subjective preference. Test and results are presented.

Proceedings Article | 29 January 2007 Paper

Image quality and automatic color equalization

M. Chambah, A. Rizzi, C. Saint Jean

Proceedings Volume 6494, 64940B (2007) https://doi.org/10.1117/12.697778

KEYWORDS: Image quality, Image filtering, Visualization, Video, Video compression, Image enhancement, Image compression, Digital imaging, Information visualization, Quality measurement

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In the professional movie field, image quality is mainly judged visually. In fact, experts and technicians judge and determine the quality of the film images during the calibration (post production) process. As a consequence, the quality of a restored movie is also estimated subjectively by experts [26,27]. On the other hand, objective quality metrics do not necessarily correlate well with perceived quality [28]. Moreover, some measures assume that there exists a reference in the form of an "original" to compare to, which prevents their use in digital restoration field, where often there is no reference to compare to. That is why subjective evaluation is the most used and most efficient approach up to now. But subjective assessment is expensive, time consuming and does not respond, hence, to the economic requirements of the field [29,25]. Thus, reliable automatic methods for visual quality assessment are needed in the field of digital film restoration. Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being. The ACE method, for Automatic Color Equalization [1,2], is an algorithm for digital images unsupervised enhancement. Like our vision system ACE is able to adapt to widely varying lighting conditions, and to extract visual information from the environment efficaciously. We present in this paper is the use of ACE as a basis of a reference free image quality metric. ACE output is an estimate of our visual perception of a scene. The assumption, tested in other papers [3,4], is that ACE enhancing images is in the way our vision system will perceive them, increases their overall perceived quality. The basic idea proposed in this paper, is that ACE output can differ from the input more or less according to the visual quality of the input image In other word, an image appears good if it is near to the visual appearance we (estimate to) have of it. Reversely bad quality images will need "more filtering". Test and results are presented.

Proceedings Article | 17 January 2006 Paper

Further image quality assessment in digital film restoration

M. Chambah, C. Saint-Jean, F. Helt, A. Rizzi

Proceedings Volume 6059, 60590S (2006) https://doi.org/10.1117/12.642414

KEYWORDS: Image quality, Video, Image processing, Cameras, Visualization, Quality measurement, Digital imaging, Statistical analysis, Quantization, Printing

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Proceedings Article | 17 January 2005 Paper

Image quality evaluation in the field of digital film restoration

M. Chambah, C. Saint-Jean, F. Helt

Proceedings Volume 5668, (2005) https://doi.org/10.1117/12.586738

KEYWORDS: Image quality, Image processing, Video, Digital image processing, Video compression, Colorimetry, Image compression, Principal component analysis, Quality measurement, Visualization

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Proceedings Article | 17 January 2005 Paper

Linear techniques for image sequence processing acceleration

Majed Chambah, Carlo Gatta, Alessandro Rizzi

Proceedings Volume 5667, (2005) https://doi.org/10.1117/12.586768

KEYWORDS: Image processing, Image filtering, Image enhancement, Video, Video processing, Principal component analysis, Optical filters, Colorimetry, RGB color model, Digital image processing

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Proceedings Article | 18 January 2004 Paper

Tuning of perceptual technique for digital movie color restoration

Alessandro Rizzi, Majed Chambah, Davide Lenza, Bernard Besserer, Daniele Marini

Proceedings Volume 5308, (2004) https://doi.org/10.1117/12.525789

KEYWORDS: Image enhancement, Colorimetry, Visual system, Digital color imaging, Image filtering, Visual process modeling, Image processing, Electronic imaging, Data corrections, Image quality

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Proceedings Article | 18 December 2003 Paper

Underwater color constancy: enhancement of automatic live fish recognition

Majed Chambah, Dahbia Semani, Arnaud Renouf, Pierre Courtellemont, Alessandro Rizzi

Proceedings Volume 5293, (2003) https://doi.org/10.1117/12.524540

KEYWORDS: Image segmentation, Video, Feature extraction, Cameras, Colorimetry, Feature selection, Imaging systems, Electronic imaging, Visual system, Image processing

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Proceedings Article | 13 January 2003 Paper

Perceptual approach for unsupervised digital color restoration of cinematographic archives

Majed Chambah, Alessandro Rizzi, Carlo Gatta, Bernard Besserer, Daniele Marini

Proceedings Volume 5008, (2003) https://doi.org/10.1117/12.472019

KEYWORDS: Data corrections, Information visualization, Visual process modeling, Visualization, Information technology, Lanthanum, Visual system, Image enhancement, Image processing, Colorimetry

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Proceedings Article | 28 December 2001 Paper

Recent progress in automatic digital restoration of color motion pictures

Majed Chambah, Bernard Besserer, Pierre Courtellemont

Proceedings Volume 4663, (2001) https://doi.org/10.1117/12.452979

KEYWORDS: Image enhancement, Image processing, Image restoration, Colorimetry, RGB color model, Principal component analysis, Cultural heritage, Digital photography, Data processing, Lanthanum

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Showing 5 of 12 publications

Conference Committee Involvement (1)

Image Quality and System Performance V

28 January 2008 | San Jose, California, United States

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