Academic literature on the topic 'Évaluation de la qualité d’image sans référence'
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Journal articles on the topic "Évaluation de la qualité d’image sans référence":
Bastianelli, Denis, and Laurent Bonnal. "Evaluation de la qualité des produits du canard gras." Revue d’élevage et de médecine vétérinaire des pays tropicaux 67, no. 3 (June 27, 2015): 135. http://dx.doi.org/10.19182/remvt.10176.
McDonald, Lynn. "Systems Science in Health—Social Services for the Elderly and the Disabled. C. Tilquin (ed.) Systems Science Press Offices, Montreal, Que. 1983, pp. 884." Canadian Journal on Aging / La Revue canadienne du vieillissement 6, no. 4 (1987): 329–33. http://dx.doi.org/10.1017/s0714980800007625.
BROCHARD, M., K. DUHEN, and D. BOICHARD. "Dossier "PhénoFinlait : Phénotypage et génotypage pour la compréhension et la maîtrise de la composition fine du lait"." INRAE Productions Animales 27, no. 4 (October 21, 2014): 251–54. http://dx.doi.org/10.20870/productions-animales.2014.27.4.3071.
Dissertations / Theses on the topic "Évaluation de la qualité d’image sans référence":
Nguyen, Tan-Sy. "A smart system for processing and analyzing gastrointestinal abnormalities in wireless capsule endoscopy." Electronic Thesis or Diss., Paris 13, 2023. http://www.theses.fr/2023PA131052.
In this thesis, we address the challenges associated with the identification and diagnosis of pathological lesions in the gastrointestinal (GI) tract. Analyzing massive amounts of visual information obtained by Wireless Capsule Endsocopy (WCE) which is an excellent tool for visualizing and examining the GI tract (including the small intestine), poses a significant burden on clinicians, leading to an increased risk of misdiagnosis.In order to alleviate this issue, we develop an intelligent system capable of automatically detecting and identifying various GI disorders. However, the limited quality of acquired images due to distortions such as noise, blur, and uneven illumination poses a significant obstacle. Consequently, image pre-processing techniques play a crucial role in improving the quality of captured frames, thereby facilitating subsequent high-level tasks like abnormality detection and classification. In order to tackle the issues associated with limitations in image quality caused by the aforementioned distortions, novel learning-based algorithms have been proposed. More precisely, recent advancements in the realm of image restoration and enhancement techniques rely on learning-based approaches that necessitate pairs of distorted and reference images for training. However, a significant challenge arises in WCE which is an excellent tool for visualizing and diagnosing GI disorders, due to the absence of a dedicated dataset for evaluating image quality. To the best of our knowledge, there currently exists no specialized dataset designed explicitly for evaluating video quality in WCE. Therefore, in response to the need for an extensive video quality assessment dataset, we first introduce the "Quality-Oriented Database for Video Capsule Endoscopy" (QVCED).Subsequently, our findings show that assessing distortion severity significantly improves image enhancement effectiveness, especially in the case of uneven illumination. To this end, we propose a novel metric dedicated to the evaluation and quantification of uneven illumination in laparoscopic or WCE images, by extracting the image's background illuminance and considering the mapping effect of Histogram Equalization. Our metric outperforms some state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods, demonstrating its superiority and competitive performance compared to Full-Reference IQA (FR-IQA) methods.After conducting the assessment step, we proceed to develop an image quality enhancement method aimed at improving the overall quality of the images. This is achieved by leveraging the cross-attention algorithm, which establishes a comprehensive connection between the extracted distortion level and the degraded regions within the images. By employing this algorithm, we are able to precisely identify and target the specific areas in the images that have been affected by distortions. This allows an appropriate enhancement tailored to each degraded region, thereby effectively improving the image quality.Following the improvement of image quality, visual features are extracted and fed into a classifier to provide a diagnosis through classification. The challenge in the WCE domain is that a significant portion of the data remains unlabeled. To overcome this challenge, we have developed an efficient method based on self-supervised learning (SSL) approach to enhance the performance of classification. The proposed method, utilizing attention-based SSL, has successfully addressed the issue of limited labeled data commonly encountered in the existing literature
Barland, Rémi. "Évaluation objective sans référence de la qualité perçue : applications aux images et vidéos compressées." Nantes, 2007. http://www.theses.fr/2007NANT2028.
The conversion to the all-digital and the development of multimedia communications produce an ever-increasing flow of information. This massive increase in the quantity of data exchanged generates a progressive saturation of the transmission networks. To deal with this situation, the compression standards seek to exploit more and more the spatial and/or temporal correlation to reduce the bit rate. The reduction of the resulting information creates visual artefacts which can deteriorate the visual content of the scene and thus cause troubles for the end-user. In order to propose the best broadcasting service, the assessment of the perceived quality is then necessary. The subjective tests which represent the reference method to quantify the perception of distortions are expensive, difficult to implement and remain inappropriate for an on-line quality assessment. In this thesis, we are interested in the most used compression standards (image or video) and have designed no-reference quality metrics based on the exploitation of the most annoying visual artefacts, such as the blocking, blurring and ringing effects. The proposed approach is modular and adapts to the considered coder and to the required ratio between computational cost and performance. For a low complexity, the metric quantifies the distortions specific to the considered coder, only exploiting the properties of the image signal. To improve the performance, to the detriment of a certain complexity, this one integrates in addition, cognitive models simulating the mechanisms of the visual attention. The saliency maps generated are then used to refine the proposed distortion measures purely based on the image signal
Ouni, Sonia. "Evaluation de la qualité des images couleur. Application à la recherche & à l'amélioration des images." Thesis, Reims, 2012. http://www.theses.fr/2012REIMS034.
The research area in the objective quality assessment of the color images has been a renewed interest in recent years. The work is primarily driven by the advent of digital pictures and additional needs in image coding (compression, transmission, recovery, indexing,...). So far the best evaluation is visual (hence subjective) or by psychophysical techniques or by expert evaluation. Therefore, it is useful, even necessary, to establish criteria and objectives that automatically measures quality scores closest possible quality scores given by the subjective evaluation. We propose, firstly, a new full reference metric to assess the quality of color images, called overall Delta E, based on color appearance and incorporates the features of the human visual system (HVS). Performance was measured in two areas of application compression and restoration. The experiments carried out show a significant correlation between the results and subjective assessment.Then, we propose a new no reference quality assessmenent color images approach based on neural networks: given the multidimensional nature of image quality, a quantification of quality has been proposed, based on a set of attributes forming the descriptor UN (Utility, Naturalness). Accuracy reflects the sharpness and clarity. As for naturality, it reflects the brightness and color. To model the criterion of color, three no reference metrics were defined to detect the dominant color in the image, the proportion of that color and its spatial dispersion. This approach is based on neural networks to mimic the HVS perception. Two variants of this approach have been tried (direct and progressive). The results showed the performance of the progressive variant compared to the direct variant. The application of the proposed approach in two areas: in the context of restoration, this approach has served as a stopping criterion for automatic restoration algorithms. In addition, we have used in a system for estimating the quality of images to automatically detect the type of content in an image degradation. In the context of indexing and image retrieval, the proposed approach was used to introduce the quality of images in the database as an index. The experimental results showed the improvement of system performance image search by content by using the index or by making a quality refinement results with the quality criterion