Codage de cartes de profondeur par deformation de courbes elastiques

In multiple-view video plus depth, depth maps can be represented by means of grayscale images and the corresponding temporal sequence can be thought as a standard grayscale video sequence. However depth maps have different properties from natural images: they present large areas of smooth surfaces separated by sharp edges. Arguably the most important information lies in object contours, as a consequence an interesting approach consists in performing a lossless coding of the contour map, possibly followed by a lossy coding of per-object depth values.In this context, we propose a new technique for the lossless coding of object contours, based on the elastic deformation of curves. A continuous evolution of elastic deformations between two reference contour curves can be modelled, and an elastically deformed version of the reference contours can be sent to the decoder with an extremely small coding cost and used as side information to improve the lossless coding of the actual contour. After the main discontinuities have been captured by the contour description, the depth field inside each region is rather smooth. We proposed and tested two different techniques for the coding of the depth field inside each region. The first technique performs the shape-adaptive wavelet transform followed by the shape-adaptive version of SPIHT. The second technique performs a prediction of the depth field from its subsampled version and the set of coded contours. It is generally recognized that a high quality view rendering at the receiver side is possible only by preserving the contour information, since distortions on edges during the encoding step would cause a sensible degradation on the synthesized view and on the 3D perception. We investigated this claim by conducting a subjective quality assessment test to compare an object-based technique and a hybrid block-based techniques for the coding of depth maps.Dans le format multiple-view video plus depth, les cartes de profondeur peuvent être représentées comme des images en niveaux de gris et la séquence temporelle correspondante peut être considérée comme une séquence vidéo standard en niveaux de gris. Cependant les cartes de profondeur ont des propriétés différentes des images naturelles: ils présentent de grandes surfaces lisses séparées par des arêtes vives. On peut dire que l'information la plus importante réside dans les contours de l'objet, en conséquence une approche intéressante consiste à effectuer un codage sans perte de la carte de contour, éventuellement suivie d'un codage lossy des valeurs de profondeur par-objet.Dans ce contexte, nous proposons une nouvelle technique pour le codage sans perte des contours de l'objet, basée sur la déformation élastique des courbes. Une évolution continue des déformations élastiques peut être modélisée entre deux courbes de référence, et une version du contour déformée élastiquement peut être envoyé au décodeur avec un coût de codage très faible et utilisé comme information latérale pour améliorer le codage sans perte du contour réel. Après que les principales discontinuités ont été capturés par la description du contour, la profondeur à l'intérieur de chaque région est assez lisse. Nous avons proposé et testé deux techniques différentes pour le codage du champ de profondeur à l'intérieur de chaque région. La première technique utilise la version adaptative à la forme de la transformation en ondelette, suivie par la version adaptative à la forme de SPIHT.La seconde technique effectue une prédiction du champ de profondeur à partir de sa version sous-échantillonnée et l'ensemble des contours codés. Il est généralement reconnu qu'un rendu de haute qualité au récepteur pour un nouveau point de vue est possible que avec la préservation de l'information de contour, car des distorsions sur les bords lors de l'étape de codage entraînerait une dégradation évidente sur la vue synthétisée et sur la perception 3D. Nous avons étudié cette affirmation en effectuant un test d'évaluation de la qualité perçue en comparant, pour le codage des cartes de profondeur, une technique basée sur la compression d'objects et une techniques de codage vidéo hybride à blocs

3D coding tools final report

Livrable D4.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.3 du projet. Son titre : 3D coding tools final repor

A Novel Inpainting Framework for Virtual View Synthesis

Multi-view imaging has stimulated significant research to enhance the user experience of free viewpoint video, allowing interactive navigation between views and the freedom to select a desired view to watch. This usually involves transmitting both textural and depth information captured from different viewpoints to the receiver, to enable the synthesis of an arbitrary view. In rendering these virtual views, perceptual holes can appear due to certain regions, hidden in the original view by a closer object, becoming visible in the virtual view. To provide a high quality experience these holes must be filled in a visually plausible way, in a process known as inpainting. This is challenging because the missing information is generally unknown and the hole-regions can be large. Recently depth-based inpainting techniques have been proposed to address this challenge and while these generally perform better than non-depth assisted methods, they are not very robust and can produce perceptual artefacts. This thesis presents a new inpainting framework that innovatively exploits depth and textural self-similarity characteristics to construct subjectively enhanced virtual viewpoints. The framework makes three significant contributions to the field: i) the exploitation of view information to jointly inpaint textural and depth hole regions; ii) the introduction of the novel concept of self-similarity characterisation which is combined with relevant depth information; and iii) an advanced self-similarity characterising scheme that automatically determines key spatial transform parameters for effective and flexible inpainting. The presented inpainting framework has been critically analysed and shown to provide superior performance both perceptually and numerically compared to existing techniques, especially in terms of lower visual artefacts. It provides a flexible robust framework to develop new inpainting strategies for the next generation of interactive multi-view technologies

Implementation of Depth Map Filtering on GPU

The thesis work was part of the Mobile 3DTV project which studied the capture, coding and transmission of 3D video representation formats in mobile delivery scenarios. The main focus of study was to determine if it was practical to transmit and view 3D videos on mobile devices. The chosen approach for virtual view synthesis was Depth Image Based Rendering (DIBR). The depth computed is often inaccurate, noisy, low in resolution or even inconsistent over a video sequence. Therefore, the sensed depth map has to be post-processed and refined through proper filtering. Bilateral filter was used for the iterative refinement process, using the information from one of the associated high quality texture (color) image (left or right view). The primary objective of this thesis was to perform the filtering operation in real-time. Therefore, we ported the algorithm to a GPU. As for the programming platform we chose OpenCL from the Khronos Group. The reason was that the platform is capable of programming on heterogeneous parallel computing environments, which means it is platform, vendor, or hardware independent. It was observed that the filtering algorithm was suitable for GPU implementation. This was because, even though every pixel used the information from its neighborhood window, processing for one pixel has no dependency on the results from its surrounding pixels. Thus, once the data for the neighborhood was loaded into the local memory of the multiprocessor, simultaneous processing for several pixels could be carried out by the device. The results obtained from our experiments were quite encouraging. We executed the MEX implementation on a Core2Duo CPU with 2 GB of RAM. On the other hand we used NVIDIA GeForce 240 as the GPU device, which comes with 96 cores, graphics clock of 550 MHz, processor clock of 1340 MHz and 512 MB memory. The processing speed improved significantly and the quality of the depth maps was at par with the same algorithm running on a CPU. In order to test the effect of our filtering algorithm on degraded depth map, we introduced artifacts by compressing it using H.264 encoder. The level of degradation was controlled by varying the quantization parameter. The blocky depth map was filtered separately using our implementation on GPU and then on CPU. The results showed improvement in speed up to 30 times, while obtaining refined depth maps with similar quality measure as the ones processed using the CPU implementation

ReSCon '10, Research Student Conference: Book of Abstracts

The third SED Research Student Conference (ReSCon2010) was hosted over three days, 21-23 June 2010, in the Hamilton Centre at Brunel University. The conference consisted of oral and poster presentations, which showcased the high quality and diversity of the research being conducted within the School of Engineering and Design. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Deep learning based objective quality assessment of multidimensional visual content

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

Image Quality Assessment for DIBR Synthesized Views using Elastic Metric

International audienc