86 research outputs found

    Perceptual video quality assessment: the journey continues!

    Get PDF
    Perceptual Video Quality Assessment (VQA) is one of the most fundamental and challenging problems in the field of Video Engineering. Along with video compression, it has become one of two dominant theoretical and algorithmic technologies in television streaming and social media. Over the last 2 decades, the volume of video traffic over the internet has grown exponentially, powered by rapid advancements in cloud services, faster video compression technologies, and increased access to high-speed, low-latency wireless internet connectivity. This has given rise to issues related to delivering extraordinary volumes of picture and video data to an increasingly sophisticated and demanding global audience. Consequently, developing algorithms to measure the quality of pictures and videos as perceived by humans has become increasingly critical since these algorithms can be used to perceptually optimize trade-offs between quality and bandwidth consumption. VQA models have evolved from algorithms developed for generic 2D videos to specialized algorithms explicitly designed for on-demand video streaming, user-generated content (UGC), virtual and augmented reality (VR and AR), cloud gaming, high dynamic range (HDR), and high frame rate (HFR) scenarios. Along the way, we also describe the advancement in algorithm design, beginning with traditional hand-crafted feature-based methods and finishing with current deep-learning models powering accurate VQA algorithms. We also discuss the evolution of Subjective Video Quality databases containing videos and human-annotated quality scores, which are the necessary tools to create, test, compare, and benchmark VQA algorithms. To finish, we discuss emerging trends in VQA algorithm design and general perspectives on the evolution of Video Quality Assessment in the foreseeable future

    FGR-Net: interpretable fundus image gradeability classification based on deep reconstruction learning

    Get PDF
    The performance of diagnostic Computer-Aided Design (CAD) systems for retinal diseases depends on the quality of the retinal images being screened. Thus, many studies have been developed to evaluate and assess the quality of such retinal images. However, most of them did not investigate the relationship between the accuracy of the developed models and the quality of the visualization of interpretability methods for distinguishing between gradable and non-gradable retinal images. Consequently, this paper presents a novel framework called ‘‘FGR-Net’’ to automatically assess and interpret underlying fundus image quality by merging an autoencoder network with a classifier network. The FGR-Net model also provides an interpretable quality assessment through visualizations. In particular, FGR-Net uses a deep autoencoder to reconstruct the input image in order to extract the visual characteristics of the input fundus images based on self-supervised learning. The extracted features by the autoencoder are then fed into a deep classifier network to distinguish between gradable and ungradable fundus images. FGR-Net is evaluated with different interpretability methods, which indicates that the autoencoder is a key factor in forcing the classifier to focus on the relevant structures of the fundus images, such as the fovea, optic disk, and prominent blood vessels. Additionally, the interpretability methods can provide visual feedback for ophthalmologists to understand how our model evaluates the quality of fundus images. The experimental results showed the superiority of FGR-Net over the state-of-the-art quality assessment methods, with an accuracy of > 89% and an F1-score of > 87%. The code is publicly available at https://github.com/saifalkh/FGR-Net.Instituto de Investigación en Informátic

    Regression-free Blind Image Quality Assessment

    Full text link
    Regression-based blind image quality assessment (IQA) models are susceptible to biased training samples, leading to a biased estimation of model parameters. To mitigate this issue, we propose a regression-free framework for image quality evaluation, which is founded upon retrieving similar instances by incorporating semantic and distortion features. The motivation behind this approach is rooted in the observation that the human visual system (HVS) has analogous visual responses to semantically similar image contents degraded by the same distortion. The proposed framework comprises two classification-based modules: semantic-based classification (SC) module and distortion-based classification (DC) module. Given a test image and an IQA database, the SC module retrieves multiple pristine images based on semantic similarity. The DC module then retrieves instances based on distortion similarity from the distorted images that correspond to each retrieved pristine image. Finally, the predicted quality score is derived by aggregating the subjective quality scores of multiple retrieved instances. Experimental results on four benchmark databases validate that the proposed model can remarkably outperform the state-of-the-art regression-based models.Comment: 11 pages, 7 figures, 50 conference

    Enhancing Mesh Deformation Realism: Dynamic Mesostructure Detailing and Procedural Microstructure Synthesis

    Get PDF
    Propomos uma solução para gerar dados de mapas de relevo dinâmicos para simular deformações em superfícies macias, com foco na pele humana. A solução incorpora a simulação de rugas ao nível mesoestrutural e utiliza texturas procedurais para adicionar detalhes de microestrutura estáticos. Oferece flexibilidade além da pele humana, permitindo a geração de padrões que imitam deformações em outros materiais macios, como couro, durante a animação. As soluções existentes para simular rugas e pistas de deformação frequentemente dependem de hardware especializado, que é dispendioso e de difícil acesso. Além disso, depender exclusivamente de dados capturados limita a direção artística e dificulta a adaptação a mudanças. Em contraste, a solução proposta permite a síntese dinâmica de texturas que se adaptam às deformações subjacentes da malha de forma fisicamente plausível. Vários métodos foram explorados para sintetizar rugas diretamente na geometria, mas sofrem de limitações como auto-interseções e maiores requisitos de armazenamento. A intervenção manual de artistas na criação de mapas de rugas e mapas de tensão permite controle, mas pode ser limitada em deformações complexas ou onde maior realismo seja necessário. O nosso trabalho destaca o potencial dos métodos procedimentais para aprimorar a geração de padrões de deformação dinâmica, incluindo rugas, com maior controle criativo e sem depender de dados capturados. A incorporação de padrões procedimentais estáticos melhora o realismo, e a abordagem pode ser estendida além da pele para outros materiais macios.We propose a solution for generating dynamic heightmap data to simulate deformations for soft surfaces, with a focus on human skin. The solution incorporates mesostructure-level wrinkles and utilizes procedural textures to add static microstructure details. It offers flexibility beyond human skin, enabling the generation of patterns mimicking deformations in other soft materials, such as leater, during animation. Existing solutions for simulating wrinkles and deformation cues often rely on specialized hardware, which is costly and not easily accessible. Moreover, relying solely on captured data limits artistic direction and hinders adaptability to changes. In contrast, our proposed solution provides dynamic texture synthesis that adapts to underlying mesh deformations. Various methods have been explored to synthesize wrinkles directly to the geometry, but they suffer from limitations such as self-intersections and increased storage requirements. Manual intervention by artists using wrinkle maps and tension maps provides control but may be limited to the physics-based simulations. Our research presents the potential of procedural methods to enhance the generation of dynamic deformation patterns, including wrinkles, with greater creative control and without reliance on captured data. Incorporating static procedural patterns improves realism, and the approach can be extended to other soft-materials beyond skin

    Efficient image-based rendering

    Get PDF
    Recent advancements in real-time ray tracing and deep learning have significantly enhanced the realism of computer-generated images. However, conventional 3D computer graphics (CG) can still be time-consuming and resource-intensive, particularly when creating photo-realistic simulations of complex or animated scenes. Image-based rendering (IBR) has emerged as an alternative approach that utilizes pre-captured images from the real world to generate realistic images in real-time, eliminating the need for extensive modeling. Although IBR has its advantages, it faces challenges in providing the same level of control over scene attributes as traditional CG pipelines and accurately reproducing complex scenes and objects with different materials, such as transparent objects. This thesis endeavors to address these issues by harnessing the power of deep learning and incorporating the fundamental principles of graphics and physical-based rendering. It offers an efficient solution that enables interactive manipulation of real-world dynamic scenes captured from sparse views, lighting positions, and times, as well as a physically-based approach that facilitates accurate reproduction of the view dependency effect resulting from the interaction between transparent objects and their surrounding environment. Additionally, this thesis develops a visibility metric that can identify artifacts in the reconstructed IBR images without observing the reference image, thereby contributing to the design of an effective IBR acquisition pipeline. Lastly, a perception-driven rendering technique is developed to provide high-fidelity visual content in virtual reality displays while retaining computational efficiency.Jüngste Fortschritte im Bereich Echtzeit-Raytracing und Deep Learning haben den Realismus computergenerierter Bilder erheblich verbessert. Konventionelle 3DComputergrafik (CG) kann jedoch nach wie vor zeit- und ressourcenintensiv sein, insbesondere bei der Erstellung fotorealistischer Simulationen von komplexen oder animierten Szenen. Das bildbasierte Rendering (IBR) hat sich als alternativer Ansatz herauskristallisiert, bei dem vorab aufgenommene Bilder aus der realen Welt verwendet werden, um realistische Bilder in Echtzeit zu erzeugen, so dass keine umfangreiche Modellierung erforderlich ist. Obwohl IBR seine Vorteile hat, ist es eine Herausforderung, das gleiche Maß an Kontrolle über Szenenattribute zu bieten wie traditionelle CG-Pipelines und komplexe Szenen und Objekte mit unterschiedlichen Materialien, wie z.B. transparente Objekte, akkurat wiederzugeben. In dieser Arbeit wird versucht, diese Probleme zu lösen, indem die Möglichkeiten des Deep Learning genutzt und die grundlegenden Prinzipien der Grafik und des physikalisch basierten Renderings einbezogen werden. Sie bietet eine effiziente Lösung, die eine interaktive Manipulation von dynamischen Szenen aus der realen Welt ermöglicht, die aus spärlichen Ansichten, Beleuchtungspositionen und Zeiten erfasst wurden, sowie einen physikalisch basierten Ansatz, der eine genaue Reproduktion des Effekts der Sichtabhängigkeit ermöglicht, der sich aus der Interaktion zwischen transparenten Objekten und ihrer Umgebung ergibt. Darüber hinaus wird in dieser Arbeit eine Sichtbarkeitsmetrik entwickelt, mit der Artefakte in den rekonstruierten IBR-Bildern identifiziert werden können, ohne das Referenzbild zu betrachten, und die somit zur Entwicklung einer effektiven IBR-Erfassungspipeline beiträgt. Schließlich wird ein wahrnehmungsgesteuertes Rendering-Verfahren entwickelt, um visuelle Inhalte in Virtual-Reality-Displays mit hoherWiedergabetreue zu liefern und gleichzeitig die Rechenleistung zu erhalten

    Image quality assessment based on the perceived structural similarity index of an image

    Get PDF
    Image quality assessment (IQA) has a very important role and wide applications in image acquisition, storage, transmission and processing. In designing IQA models, human visual system (HVS) characteristics introduced play an important role in improving their performances. In this paper, combining image distortion characteristics with HVS characteristics, based on the structure similarity index (SSIM) model, a novel IQA model based on the perceived structure similarity index (PSIM) of image is proposed. In the method, first, a perception model for HVS perceiving real images is proposed, combining the contrast sensitivity, frequency sensitivity, luminance nonlinearity and masking characteristics of HVS; then, in order to simulate HVS perceiving real image, the real images are processed with the proposed perception model, to eliminate their visual redundancy, thus, the perceived images are obtained; finally, based on the idea and modeling method of SSIM, combining with the features of perceived image, a novel IQA model, namely PSIM, is proposed. Further, in order to illustrate the performance of PSIM, 5335 distorted images with 41 distortion types in four image databases (TID2013, CSIQ, LIVE and CID) are used to simulate from three aspects: overall IQA of each database, IQA for each distortion type of images, and IQA for special distortion types of images. Further, according to the comprehensive benefit of precision, generalization performance and complexity, their IQA results are compared with those of 12 existing IQA models. The experimental results show that the accuracy (PLCC) of PSIM is 9.91% higher than that of SSIM in four databases, on average; and its performance is better than that of 12 existing IQA models. Synthesizing experimental results and theoretical analysis, it is showed that the proposed PSIM model is an effective and excellent IQA model

    Deep learning based objective quality assessment of multidimensional visual content

    Get PDF
    Tese (doutorado) — Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2022.Na última década, houve um tremendo aumento na popularidade dos aplicativos multimídia, aumentando assim o conteúdo multimídia. Quando esses conteúdossão gerados, transmitidos, reconstruídos e compartilhados, seus valores de pixel originais são transformados. Nesse cenário, torna-se mais crucial e exigente avaliar a qualidade visual do conteúdo visual afetado para que os requisitos dos usuários finais sejam atendidos. Neste trabalho, investigamos recursos espaciais, temporais e angulares eficazes desenvolvendo algoritmos sem referência que avaliam a qualidade visual de conteúdo visual multidimensional distorcido. Usamos algoritmos de aprendizado de máquina e aprendizado profundo para obter precisão de previsão.Para avaliação de qualidade de imagem bidimensional (2D), usamos padrões binários locais multiescala e informações de saliência e treinamos/testamos esses recursos usando o Random Forest Regressor. Para avaliação de qualidade de vídeo 2D, apresentamos um novo conceito de saliência espacial e temporal e pontuações de qualidade objetivas personalizadas. Usamos um modelo leve baseado em Rede Neural Convolucional (CNN) para treinamento e teste em patches selecionados de quadros de vídeo.Para avaliação objetiva da qualidade de imagens de campo de luz (LFI) em quatro dimensões (4D), propomos sete métodos de avaliação de qualidade LFI (LF-IQA) no total. Considerando que o LFI é composto por multi-views densas, Inspired by Human Visual System (HVS), propomos nosso primeiro método LF-IQA que é baseado em uma arquitetura CNN de dois fluxos. O segundo e terceiro métodos LF-IQA também são baseados em uma arquitetura de dois fluxos, que incorpora CNN, Long Short-Term Memory (LSTM) e diversos recursos de gargalo. O quarto LF-IQA é baseado nas camadas CNN e Atrous Convolution (ACL), enquanto o quinto método usa as camadas CNN, ACL e LSTM. O sexto método LF-IQA também é baseado em uma arquitetura de dois fluxos, na qual EPIs horizontais e verticais são processados no domínio da frequência. Por último, mas não menos importante, o sétimo método LF-IQA é baseado em uma Rede Neural Convolucional de Gráfico. Para todos os métodos mencionados acima, realizamos experimentos intensivos e os resultados mostram que esses métodos superaram os métodos de última geração em conjuntos de dados de qualidade populares.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).In the last decade, there has been a tremendous increase in the popularity of multimedia applications, hence increasing multimedia content. When these contents are generated, transmitted, reconstructed and shared, their original pixel values are transformed. In this scenario, it becomes more crucial and demanding to assess visual quality of the affected visual content so that the requirements of end-users are satisfied. In this work, we investigate effective spatial, temporal, and angular features by developing no-reference algorithms that assess the visual quality of distorted multi-dimensional visual content. We use machine learning and deep learning algorithms to obtain prediction accuracy. For two-dimensional (2D) image quality assessment, we use multiscale local binary patterns and saliency information, and train / test these features using Random Forest Regressor. For 2D video quality assessment, we introduce a novel concept of spatial and temporal saliency and custom objective quality scores. We use a Convolutional Neural Network (CNN) based light-weight model for training and testing on selected patches of video frames. For objective quality assessment of four-dimensional (4D) light field images (LFI), we propose seven LFI quality assessment (LF-IQA) methods in total. Considering that LFI is composed of dense multi-views, Inspired by Human Visual System (HVS), we propose our first LF-IQA method that is based on a two-streams CNN architecture. The second and third LF-IQA methods are also based on a two-stream architecture, which incorporates CNN, Long Short-Term Memory (LSTM), and diverse bottleneck features. The fourth LF-IQA is based on CNN and Atrous Convolution layers (ACL), while the fifth method uses CNN, ACL, and LSTM layers. The sixth LF-IQA method is also based on a two-stream architecture, in which, horizontal and vertical EPIs are processed in the frequency domain. Last, but not least, the seventh LF-IQA method is based on a Graph Convolutional Neural Network. For all of the methods mentioned above, we performed intensive experiments, and the results show that these methods outperformed state-of-the-art methods on popular quality datasets

    Multimodal Adversarial Learning

    Get PDF
    Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models are susceptible to adversarial attacks. In the case of generative models, facial sketches drawn by artists mostly used by law enforcement agencies depend on the ability of the artist to clearly replicate all the key facial features that aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. However, the incorporation of hybrid discriminators, which perform attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network have shown to be powerful tools towards better multi modal learning techniques. In general, our overall approach was aimed at improving target detection systems and the visual appeal of synthesized images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesized image. We synthesized sketches using XDOG filter for the CelebA, Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results overall for different model applications are impressive compared to current state of the art

    Blind image quality assessment: from heuristic-based to learning-based

    Get PDF
    Image quality assessment (IQA) plays an important role in numerous digital image processing applications, including image compression, image transmission, and image restoration, etc. The goal of objective IQA is to develop computational models that can predict image quality in a way being consistent with human perception. Compared with subjective quality evaluations such as psycho-visual tests, objective IQA metrics have the advantages of predicting image quality automatically and effectively in a timely manner. This thesis focuses on a particular type of objective IQA – blind IQA (BIQA), where the developed methods not only achieve objective IQA, but also are able to assess the perceptual quality of digital images without access to their pristine reference counterparts. Firstly, a novel blind image sharpness evaluator is introduced in Chapter 3, which leverages the discrepancy measures of structural degradation. Secondly, a “completely blind” quality assessment metric for gamut-mapped images is designed in Chapter 4, which does not need subjective quality scores during the model training. Thirdly, a general-purpose BIQA method is presented in Chapter 5, which can evaluate the quality of digital images without prior knowledge on the types of distortions. Finally, in Chapter 6, a deep neural network-based general-purpose BIQA method is proposed, which is fully data driven and trained in an end-to-end manner. In summary, four BIQA methods are introduced in this thesis, where the first three are heuristic-based and the last one is learning-based. Unlike heuristics-based ones, the learning-based method does not involves manually engineered feature designs

    Effective Features for No-Reference Image Quality Assessment on Mobile Devices

    Get PDF
    The goal of this thesis is the analysis and development of a no-reference image quality assessment algorithm. Algorithms of this kind are increasingly employed in multimedia applications with the aim of delivering higher quality of service. In order to achieve the goal, a state-of-art no-reference algorithm was used as a groundwork to improve. The proposed model is intended to be deployed in low-resources mobile devices such as smartphones and tablet
    corecore