1,009 research outputs found
Image segmentation with adaptive region growing based on a polynomial surface model
A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the Lâ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces
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Novel entropy coding and its application of the compression of 3D image and video signals
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe broadcast industry is moving future Digital Television towards Super high resolution TV (4k or 8k) and/or 3D TV. This ultimately will increase the demand on data rate and subsequently the demand for highly efficient codecs. One of the techniques that researchers found it one of the promising technologies in the industry in the next few years is 3D Integral Image and Video due to its simplicity and mimics the reality, independently on viewer aid, one of the challenges of the 3D Integral technology is to improve the compression algorithms to adequate the high resolution and exploit the advantages of the characteristics of this technology. The research scope of this thesis includes designing a novel coding for the 3D Integral image and video compression. Firstly to address the compression of 3D Integral imaging the research proposes novel entropy coding which will be implemented first on 2D traditional images content in order to compare it with the other traditional common standards then will be applied on 3D Integra image and video. This approach seeks to achieve high performance represented by high image quality and low bit rate in association with low computational complexity. Secondly, new algorithm will be proposed in an attempt to improve and develop the transform techniques performance, initially by using a new adaptive 3D-DCT algorithm then by proposing a new hybrid 3D DWT-DCT algorithm via exploiting the advantages of each technique and get rid of the artifact that each technique of them suffers from. Finally, the proposed entropy coding will be further implemented to the 3D integral video in association with another proposed algorithm that based on calculating the motion vector on the average viewpoint for each frame. This approach seeks to minimize the complexity and reduce the speed without affecting the Human Visual System (HVS) performance. Number of block matching techniques will be used to investigate the best block matching technique that is adequate for the new proposed 3D integral video algorithm
State of the art in 2D content representation and compression
Livrable D1.3 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 D3.1 du projet
Survey of Error Concealment techniques: Research directions and open issues
© 2015 IEEE. Error Concealment (EC) techniques use either spatial, temporal or a combination of both types of information to recover the data lost in transmitted video. In this paper, existing EC techniques are reviewed, which are divided into three categories, namely Intra-frame EC, Inter-frame EC, and Hybrid EC techniques. We first focus on the EC techniques developed for the H.264/AVC standard. The advantages and disadvantages of these EC techniques are summarized with respect to the features in H.264. Then, the EC algorithms are also analyzed. These EC algorithms have been recently adopted in the newly introduced H.265/HEVC standard. A performance comparison between the classic EC techniques developed for H.264 and H.265 is performed in terms of the average PSNR. Lastly, open issues in the EC domain are addressed for future research consideration
A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding
Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression
Nouvelles mĂ©thodes de prĂ©diction inter-images pour la compression dâimages et de vidĂ©os
Due to the large availability of video cameras and new social media practices, as well as the emergence of cloud services, images and videosconstitute today a significant amount of the total data that is transmitted over the internet. Video streaming applications account for more than 70% of the world internet bandwidth. Whereas billions of images are already stored in the cloud and millions are uploaded every day. The ever growing streaming and storage requirements of these media require the constant improvements of image and video coding tools. This thesis aims at exploring novel approaches for improving current inter-prediction methods. Such methods leverage redundancies between similar frames, and were originally developed in the context of video compression. In a first approach, novel global and local inter-prediction tools are associated to improve the efficiency of image sets compression schemes based on video codecs. By leveraging a global geometric and photometric compensation with a locally linear prediction, significant improvements can be obtained. A second approach is then proposed which introduces a region-based inter-prediction scheme. The proposed method is able to improve the coding performances compared to existing solutions by estimating and compensating geometric and photometric distortions on a semi-local level. This approach is then adapted and validated in the context of video compression. Bit-rate improvements are obtained, especially for sequences displaying complex real-world motions such as zooms and rotations. The last part of the thesis focuses on deep learning approaches for inter-prediction. Deep neural networks have shown striking results for a large number of computer vision tasks over the last years. Deep learning based methods proposed for frame interpolation applications are studied here in the context of video compression. Coding performance improvements over traditional motion estimation and compensation methods highlight the potential of these deep architectures.En raison de la grande disponibilitĂ© des dispositifs de capture vidĂ©o et des nouvelles pratiques liĂ©es aux rĂ©seaux sociaux, ainsi quâĂ lâĂ©mergence desservices en ligne, les images et les vidĂ©os constituent aujourdâhui une partie importante de donnĂ©es transmises sur internet. Les applications de streaming vidĂ©o reprĂ©sentent ainsi plus de 70% de la bande passante totale de lâinternet. Des milliards dâimages sont dĂ©jĂ stockĂ©es dans le cloud et des millions y sont tĂ©lĂ©chargĂ©s chaque jour. Les besoins toujours croissants en streaming et stockage nĂ©cessitent donc une amĂ©lioration constante des outils de compression dâimage et de vidĂ©o. Cette thĂšse vise Ă explorer des nouvelles approches pour amĂ©liorer les mĂ©thodes actuelles de prĂ©diction inter-images. De telles mĂ©thodes tirent parti des redondances entre images similaires, et ont Ă©tĂ© dĂ©veloppĂ©es Ă lâorigine dans le contexte de la vidĂ©o compression. Dans une premiĂšre partie, de nouveaux outils de prĂ©diction inter globaux et locaux sont associĂ©s pour amĂ©liorer lâefficacitĂ© des schĂ©mas de compression de bases de donnĂ©es dâimage. En associant une compensation gĂ©omĂ©trique et photomĂ©trique globale avec une prĂ©diction linĂ©aire locale, des amĂ©liorations significatives peuvent ĂȘtre obtenues. Une seconde approche est ensuite proposĂ©e qui introduit un schĂ©ma deprĂ©diction inter par rĂ©gions. La mĂ©thode proposĂ©e est en mesure dâamĂ©liorer les performances de codage par rapport aux solutions existantes en estimant et en compensant les distorsions gĂ©omĂ©triques et photomĂ©triques Ă une Ă©chelle semi locale. Cette approche est ensuite adaptĂ©e et validĂ©e dans le cadre de la compression vidĂ©o. Des amĂ©liorations en rĂ©duction de dĂ©bit sont obtenues, en particulier pour les sĂ©quences prĂ©sentant des mouvements complexes rĂ©els tels que des zooms et des rotations. La derniĂšre partie de la thĂšse se concentre sur lâĂ©tude des mĂ©thodes dâapprentissage en profondeur dans le cadre de la prĂ©diction inter. Ces derniĂšres annĂ©es, les rĂ©seaux de neurones profonds ont obtenu des rĂ©sultats impressionnants pour un grand nombre de tĂąches de vision par ordinateur. Les mĂ©thodes basĂ©es sur lâapprentissage en profondeur proposĂ©esĂ lâorigine pour de lâinterpolation dâimages sont Ă©tudiĂ©es ici dans le contexte de la compression vidĂ©o. Des amĂ©liorations en terme de performances de codage sont obtenues par rapport aux mĂ©thodes dâestimation et de compensation de mouvements traditionnelles. Ces rĂ©sultats mettent en Ă©vidence le fort potentiel de ces architectures profondes dans le domaine de la compression vidĂ©o
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