Academic literature on the topic 'Subjective and Objective Image Quality Assessment'

Les images de champs de lumière (LFI) suscitent un intérêt et une fascination remarquables en raison de leur importance croissante dans les applications immersives. Étant donné que les LFI peuvent être déformés à différentes étapes, de l'acquisition à la visualisation, l'évaluation de la qualité des images de champs de lumière (LFIQA) est d'une importance vitale pour surveiller les dégradations potentielles de la qualité des LFI.La première contribution (Chapitre 3) de ce travail se concentre sur le développement de deux métriques LFIQA sans référence (NR) fondées sur des caractéristiques ad-hoc, dans lesquelles les informations de texture et les coefficients ondelettes sont exploités pour l'évaluation de la qualité.Puis dans la deuxième partie (Chapitre 4), nous explorons le potentiel de la technologie de l’apprentissage profond (deep learning) pour l'évaluation de la qualité des LFI, et nous proposons quatre métriques LFIQA basées sur l’apprentissage profond, dont trois métriques sans référence (NR) et une métrique Full-Reference (FR).Dans la dernière partie (Chapitre 5), nous menons des évaluations subjectives et proposons une nouvelle base de données normalisée pour la LFIQA. De plus, nous fournissons une étude comparative (benchmark) de nombreuses métriques objectives LFIQA de l’état de l’art, sur la base de données proposée<br>Light Field Image (LFI) has garnered remarkable interest and fascination due to its burgeoning significance in immersive applications. Since LFIs may be distorted at various stages from acquisition to visualization, Light Field Image Quality Assessment (LFIQA) is vitally important to monitor the potential impairments of LFI quality. The first contribution (Chapter 3) of this work focuses on developing two handcrafted feature-based No-Reference (NR) LFIQA metrics, in which texture information and wavelet information are exploited for quality evaluation. Then in the second part (Chapter 4), we explore the potential of combining deep learning technology with the quality assessment of LFIs, and propose four deep learning-based LFIQA metrics according to different LFI characteristics, including three NR metrics and one Full-Reference (FR) metric. In the last part (Chapter 5), we conduct subjective experiments and propose a novel standard LFIQA database. Moreover, a benchmark of numerous state-of-the-art objective LFIQA metrics on the proposed database is provided

Au cours de la dernière décennie, la technologie de l’image et de la vidéo à haute gamme dynamique (High dynamic range - HDR) a attiré beaucoup d’attention, en particulier dans la communauté multimédia. Les progrés technologiques récents ont facilité l’acquisition, la compression et la reproduction du contenu HDR, ce qui a mené à la commercialisation des écrans HDR et à la popularisation du contenu HDR. Dans ce contexte, la mesure de la qualité du contenu HDR joue un rôle fondamental dans l’amélioration de la chaîne de distribution du contenu ainsi que des opérations qui la composent, telles que la compression et l’affichage. Cependant, l’évaluation de la qualité visuelle HDR présente de nouveaux défis par rapport au contenu à gamme dynamique standard (Standard dynamic range -SDR). Le premier défi concerne les nouvelles conditions introduites par la reproduction du contenu HDR, par ex. l’augmentation de la luminosité et du contraste. Même si une reproduction exacte de la luminance d’une scène n’est pas nécessaire pour la plupart des cas pratiques, une estimation précise de la luminance émise est cependant nécessaire pour les mesures d’évaluation objectives de la qualité HDR. Afin de comprendre les effets du rendu d’affichage sur la perception de la qualité, un algorithme permettant de reproduire très précisement une image HDR a été développé et une expérience subjective a été menée pour analyser l’impact de différents rendus sur l’évaluation subjective et objective de la qualité HDR. En outre, afin de comprendre l’impact de la couleur avec la luminosité accrue des écrans HDR, les effets des différents espaces de couleurs sur les performances de compression vidéo HDR ont également été analysés dans une autre étude subjective. Un autre défi consiste à estimer objectivement la qualité du contenu HDR, en utilisant des ordinateurs et des algorithmes. Afin de relever ce défi, la thèse procède à l’évaluation des performances des métriques de qualité d’image HDR avec référence (Full reference-FR). Les images HDR ont une plus grande plage de luminosité et des valeurs de contraste plus élevées. Etant donné que la plupart des métriques de qualité d’image sont développées pour les images SDR, elles doivent être adaptées afin d’estimer la qualité des images HDR. Différentes méthodes d’adaptation ont été utilisées pour les mesures SDR, et elles ont été comparées avec les métriques de qualité d’image existantes développées exclusivement pour les images HDR. De plus, nous proposons une nouvelle méthode d’évaluation des métriques objectives basée sur une nouvelle approche de classification. Enfin, nous comparons les scores de qualité subjectifs acquis en utilisant différentes méthodologies de test subjectives. L’évaluation subjective de la qualité est considérée comme le moyen le plus efficace et le plus fiable d’obtenir des scores de qualité «vérité-terrain» pour les stimuli sélectionnés, et les scores moyens d’opinion (Mean opinion scores-MOS) obtenus sont les valeurs auxquelles les métriques objectives sont entraînées pour correspondre. En fait, de fortes divergences peuvent facilement être rencontrés lorsque différentes bases de données de qualité multimédia sont considérées. Afin de comprendre la relation entre les valeurs de qualité acquises à l’aide de différentes méthodologies, la relation entre les valeurs MOS et les résultats des comparaisons par paires rééchellonés (Pairwise comparisons - PC) a été comparée. A cette fin, une série d’expériences ont été menées entre les méthodologies double stimulus impairment scale (DSIS) et des comparaisons par paires. Nous proposons d’inclure des comparaisons inter-contenu dans les expériences PC afin d’améliorer les performances de rééchelonnement et de réduire la variance inter-contenu ainsi que les intervalles de confiance. Les scores de PC rééchellonés peuvent également être utilisés pour des scénarios subjectifs d’évaluation de la qualité multimédia autres que le HDR<br>In the last decade, high dynamic range (HDR) image and video technology gained a lot of attention, especially within the multimedia community. Recent technological advancements made the acquisition, compression, and reproduction of HDR content easier, and that led to the commercialization of HDR displays and popularization of HDR content. In this context, measuring the quality of HDR content plays a fundamental role in improving the content distribution chain as well as individual parts of it, such as compression and display. However, HDR visual quality assessment presents new challenges with respect to the standard dynamic range (SDR) case. The first challenge is the new conditions introduced by the reproduction of HDR content, e.g. the increase in brightness and contrast. Even though accurate reproduction is not necessary for most of the practical cases, accurate estimation of the emitted luminance is necessary for the objective HDR quality assessment metrics. In order to understand the effects of display rendering on the quality perception, an accurate HDR frame reproduction algorithm was developed, and a subjective experiment was conducted to analyze the impact of different display renderings on subjective and objective HDR quality evaluation. Additionally, in order to understand the impact of color with the increased brightness of the HDR displays, the effects of different color spaces on the HDR video compression performance were also analyzed in another subjective study. Another challenge is to estimate the quality of HDR content objectively, using computers and algorithms. In order to address this challenge, the thesis proceeds with the performance evaluation of full-reference (FR) HDR image quality metrics. HDR images have a larger brightness range and higher contrast values. Since most of the image quality metrics are developed for SDR images, they need to be adapted in order to estimate the quality of HDR images. Different adaptation methods were used for SDR metrics, and they were compared with the existing image quality metrics developed exclusively for HDR images. Moreover, we propose a new method for the evaluation of metric discriminability based ona novel classification approach. Motivated by the need to fuse several different quality databases, in the third part of the thesis, we compare subjective quality scores acquired by using different subjective test methodologies. Subjective quality assessment is regarded as the most effective and reliable way of obtaining “ground-truth” quality scores for the selected stimuli, and the obtained mean opinion scores (MOS) are the values to which generally objective metrics are trained to match. In fact, strong discrepancies can easily be notified across databases when different multimedia quality databases are considered. In order to understand the relationship between the quality values acquired using different methodologies, the relationship between MOS values and pairwise comparisons (PC) scaling results were compared. For this purpose, a series of experiments were conducted using double stimulus impairment scale (DSIS) and pairwise comparisons subjective methodologies. We propose to include cross-content comparisons in the PC experiments in order to improve scaling performance and reduce cross-content variance as well as confidence intervals. The scaled PC scores can also be used for subjective multimedia quality assessment scenarios other than HDR

In theoretical part Diploma thesis deals with the model of human vision HVS (Human Visual System), which can be used for image quality assessment in TV technique area. It has been described calculations of selected JND (Just Noticeable Difference) metrics, used in evaluation of HVS. In practical part of the thesis it has been suggested and realized simulation model in Matlab, which may be used for evaluation of three JND metrics from color and grayscale images and evaluation in spatial a frequency domain. Results of JND models have been compared to another objective image quality evaluation metrics (MSE, NMSE, SNR and PSNR). For interpretation of dependencies it has been used images with different defined content.

Enhancing low light videos has been quite a challenge over the years. A video taken in low light always has the issues of low dynamic range and high noise. This master thesis presents contribution within the field of low light video enhancement. Three models are proposed with different tone mapping algorithms for extremely low light low quality video enhancement. For temporal noise removal, a motion compensated kalman structure is presented. Dynamic range of the low light video is stretched using three different methods. In Model 1, dynamic range is increased by adjustment of RGB histograms using gamma correction with a modified version of adaptive clipping thresholds. In Model 2, a shape preserving dynamic range stretch of the RGB histogram is applied using SMQT. In Model 3, contrast enhancement is done using CLAHE. In the final stage, the residual noise is removed using an efficient NLM. The performance of the models are compared on various Objective VQA metrics like NIQE, GCF and SSIM. To evaluate the actual performance of the models subjective tests are conducted, due to the large number of applications that target humans as the end user of the video.The performance of the three models are compared for a total of ten real time input videos taken in extremely low light environment. A total of 25 human observers subjectively evaluated the performance of the three models based on the parameters: contrast, visibility, visually pleasing, amount of noise and overall quality. A detailed statistical evaluation of the relative performance of the three models is also provided.

Dans le domaine de l’informatique graphique, les données tridimensionnelles, généralement représentées par des maillages triangulaires, sont employées dans une grande variété d’applications (par exemple, le lissage, la compression, le remaillage, la simplification, le rendu, etc.). Cependant, ces procédés introduisent inévitablement des artefacts qui altèrent la qualité visuelle des données 3D rendues. Ainsi, afin de guider perceptuellement les algorithmes de traitement, il y a un besoin croissant d'évaluations subjectives et objectives de la qualité visuelle à la fois performantes et adaptées, pour évaluer et prédire les artefacts visuels. Dans cette thèse, nous présentons d'abord une étude exhaustive sur les différentes sources d'artefacts associés aux données numériques graphiques, ainsi que l’évaluation objective et subjective de la qualité visuelle des artefacts. Ensuite, nous introduisons une nouvelle étude sur la qualité subjective conçue sur la base de l’évaluations de la visibilité locale des artefacts géométriques, dans laquelle il a été demandé à des observateurs de marquer les zones de maillages 3D qui contiennent des distorsions visibles. Les cartes de distorsion visuelle collectées sont utilisées pour illustrer plusieurs fonctionnalités perceptuelles du système visuel humain (HVS), et servent de vérité-terrain pour évaluer les performances des attributs et des mesures géométriques bien connus pour prédire la visibilité locale des distorsions. Notre deuxième étude vise à évaluer la qualité visuelle de modèles 3D texturés, subjectivement et objectivement. Pour atteindre ces objectifs, nous avons introduit 136 modèles traités avec à la fois des distorsions géométriques et de texture, mené une expérience subjective de comparaison par paires, et invité 101 sujets pour évaluer les qualités visuelles des modèles à travers deux protocoles de rendu. Motivés par les opinions subjectives collectées, nous proposons deux mesures de qualité visuelle objective pour les maillages texturés, en se fondant sur les combinaisons optimales des mesures de qualité issues de la géométrie et de la texture. Ces mesures de perception proposées surpassent leurs homologues en termes de corrélation avec le jugement humain<br>In computer graphics realm, three-dimensional graphical data, generally represented by triangular meshes, have become commonplace, and are deployed in a variety of application processes (e.g., smoothing, compression, remeshing, simplification, rendering, etc.). However, these processes inevitably introduce artifacts, altering the visual quality of the rendered 3D data. Thus, in order to perceptually drive the processing algorithms, there is an increasing need for efficient and effective subjective and objective visual quality assessments to evaluate and predict the visual artifacts. In this thesis, we first present a comprehensive survey on different sources of artifacts in digital graphics, and current objective and subjective visual quality assessments of the artifacts. Then, we introduce a newly designed subjective quality study based on evaluations of the local visibility of geometric artifacts, in which observers were asked to mark areas of 3D meshes that contain noticeable distortions. The collected perceived distortion maps are used to illustrate several perceptual functionalities of the human visual system (HVS), and serve as ground-truth to evaluate the performances of well-known geometric attributes and metrics for predicting the local visibility of distortions. Our second study aims to evaluate the visual quality of texture mapped 3D model subjectively and objectively. To achieve these goals, we introduced 136 processed models with both geometric and texture distortions, conducted a paired-comparison subjective experiment, and invited 101 subjects to evaluate the visual qualities of the models under two rendering protocols. Driven by the collected subjective opinions, we propose two objective visual quality metrics for textured meshes, relying on the optimal combinations of geometry and texture quality measures. These proposed perceptual metrics outperform their counterparts in term of the correlation with the human judgment

The overwhelming trend of the usage of multimedia services has raised the consumers&apos; awareness about quality. Both service providers and consumers are interested in the delivered level of perceptual quality. The perceptual quality of an original video signal can get degraded due to compression and due to its transmission over a lossy network. Video quality assessment (VQA) has to be performed in order to gauge the level of video quality. Generally, it can be performed by following subjective methods, where a panel of humans judges the quality of video, or by using objective methods, where a computational model yields an estimate of the quality. Objective methods and specifically No-Reference (NR) or Reduced-Reference (RR) methods are preferable because they are practical for implementation in real-time scenarios. This doctoral thesis begins with a review of existing approaches proposed in the area of NR image and video quality assessment. In the review, recently proposed methods of visual quality assessment are classified into three categories. This is followed by the chapters related to the description of studies on the development of NR and RR methods as well as on conducting subjective experiments of VQA. In the case of NR methods, the required features are extracted from the coded bitstream of a video, and in the case of RR methods additional pixel-based information is used. Specifically, NR methods are developed with the help of suitable techniques of regression using artificial neural networks and least-squares support vector machines. Subsequently, in a later study, linear regression techniques are used to elaborate the interpretability of NR and RR models with respect to the selection of perceptually significant features. The presented studies on subjective experiments are performed using laboratory based and crowdsourcing platforms. In the laboratory based experiments, the focus has been on using standardized methods in order to generate datasets that can be used to validate objective methods of VQA. The subjective experiments performed through crowdsourcing relate to the investigation of non-standard methods in order to determine perceptual preference of various adaptation scenarios in the context of adaptive streaming of high-definition videos. Lastly, the use of adaptive gain equalizer in the modulation frequency domain for speech enhancement has been examined. To this end, two methods of demodulating speech signals namely spectral center of gravity carrier estimation and convex optimization have been studied.

Le niveau d'exigence minimum pour tout système 3D (images stéréoscopiques) est de garantir le confort visuel des utilisateurs. Le confort visuel est un des trois axes perceptuels de la qualité d'expérience (QoE) 3D qui peut être directement lié aux paramètres techniques du système 3D. Par conséquent, le but de cette thèse est de caractériser objectivement l'impact de ces paramètres sur la perception humaine afin de contrôler la qualité stéréoscopique. La première partie de la thèse examine l'intérêt de prendre en compte l'attention visuelle des spectateurs dans la conception d'une mesure objective de qualité 3D. Premièrement, l'attention visuelle en 2D et 3D sont comparées en utilisant des stimuli simples. Les conclusions de cette première expérience sont validées en utilisant des scènes complexes avec des disparités croisées et décroisées. De plus, nous explorons l'impact de l'inconfort visuel causé par des disparités excessives sur l'attention visuelle. La seconde partie de la thèse est dédiée à la conception d'un modèle objectif de QoE pour des vidéos 3D, basé sur les seuils perceptuels humains et le niveau d'acceptabilité. De plus nous explorons la possibilité d'utiliser la modèle proposé comme une nouvelle échelle subjective. Pour la validation de ce modèle, des expériences subjectives sont conduites présentant aux sujets des images stéréoscopiques fixes et animées avec différents niveaux d'asymétrie. La performance est évaluée en comparant des prédictions objectives avec des notes subjectives pour différents niveaux d'asymétrie qui pourraient provoquer un inconfort visuel<br>The minimum requirement for any 3D (stereoscopic images) system is to guarantee visual comfort of viewers. Visual comfort is one of the three primary perceptual attributes of 3D QoE, which can be linked directly with technical parameters of a 3D system. Therefore, the goal of this thesis is to characterize objectively the impact of these parameters on human perception for stereoscopic quality monitoring. The first part of the thesis investigates whether visual attention of the viewers should be considered when designing an objective 3D quality metrics. First, the visual attention in 2D and 3D is compared using simple test patterns. The conclusions of this first experiment are validated using complex stimuli with crossed and uncrossed disparities. In addition, we explore the impact of visual discomfort caused by excessive disparities on visual attention. The second part of the thesis is dedicated to the design of an objective model of 3D video QoE, which is based on human perceptual thresholds and acceptability level. Additionally we explore the possibility to use the proposed model as a new subjective scale. For the validation of proposed model, subjective experiments with fully controlled still and moving stereoscopic images with different types of view asymmetries are conducted. The performance is evaluated by comparing objective predictions with subjective scores for various levels of view discrepancies which might provoke visual discomfort

A dominant source of Internet traffic, today, is constituted of compressed images. In modern multimedia communications, image compression plays an important role. Some of the image compression standards set by the Joint Photographic Expert Group (JPEG) include JPEG and JPEG2000. The expert group came up with the JPEG image compression standard so that still pictures could be compressed to be sent over an e-mail, be displayed on a webpage, and make high-resolution digital photography possible. This standard was originally based on a mathematical method, used to convert a sequence of data to the frequency domain, called the Discrete Cosine Transform (DCT). In the year 2000, however, a new standard was proposed by the expert group which came to be known as JPEG2000. The difference between the two is that the latter is capable of providing better compression efficiency. There is also a downside to this new format introduced. The computation required for achieving the same sort of compression efficiency as one would get with the original JPEG format is higher. JPEG is a lossy compression standard which can throw away some less important information without causing any noticeable perception differences. Whereas, in lossless compression, the primary purpose is to reduce the number of bits required to represent the original image samples without any loss of information. The areas of application of the JPEG image compression standard include the Internet, digital cameras, printing, and scanning peripherals. In this thesis work, a simulator kind of functionality setup is needed for conducting the objective quality assessment. An image is given as an input to our wireless communication system and its data size is varied (e.g. 5%, 10%, 15%, etc) and a Signal-to-Noise Ratio (SNR) value is given as input, for JPEG2000 compression. Then, this compressed image is passed through a JPEG encoder and then transmitted over a Rayleigh fading channel. The corresponding image obtained after having applied these constraints on the original image is then decoded at the receiver and inverse discrete wavelet transform (IDWT) is applied to inverse the JPEG 2000 compression. Quantization is done for the coefficients which are scalar-quantized to reduce the number of bits to represent them, without the loss of quality of the image. Then the final image is displayed on the screen. The original input image is co-passed with the images of varying data size for an SNR value at the receiver after decoding. In particular, objective perceptual quality assessment through Structural Similarity (SSIM) index using MATLAB is provided.