Grammar-Based Representations of Large Sparse Binary Matrices

Large sparse matrices representation is a fundamental problem in big data processing and analysis. In some applications dealing with large sparse matrices, the I/O of these sparse matrices is the bottleneck of the whole system. To reduce the requirement of memory bandwidth in this scenario, it is important to develop alternative compact representations of large sparse matrices, while facilitating, if possible, matrix operations. In this thesis, we propose two grammar-based methods to compactly represent a sparse binary matrix with the capability of random accessing an element in the matrix. The first approach combines dimension coding (proposed by Yang[12]) with one of raster scan or Hilbert scan, where the so-called directionless grammar is applied. With the power of scanning, dimension coding’s capability of representing 1-D sparse signals can be extended to 2-D sparse matrices. This approach inherits the random accessibility of dimension coding. In the second approach, we will introduce a new concept called Context-free Bipartite Grammar (CFBG) and present a framework wherein large sparse binary matrices can be represented by CFBG. Similar to the traditional concept of Context-free Grammar (CFG), a CFBG consists of a set of production rules. Unlike CFGs, however, the right member of each production rule in a CFBG is a labeled bipartite graph with each edge labeled either as a variable or terminal symbol. As the right-hand side of a production rule is an ordered edge set, CFBG is also directionless. Two bipartite grammar transforms, a Sequential D-Neighborhood Pairing Transform (SNPT) and an Iterative Pairing Transform (IPT), are further presented to convert any binary matrix into a CFBG representing it. Experiments show that compared with popular sparse matrix storage methods such as compressed row storage and quadtree, grammar-based sparse binary matrix representations can reduce the storage requirement of sparse matrices significantly (by a factor of as much as 70)

Efficient depth image compression using accurate depth discontinuity detection and prediction

This paper presents a novel depth image compression algorithm for both 3D Television (3DTV) and Free Viewpoint Television (FVTV) services. The proposed scheme adopts the K-means clustering algorithm to segment the depth image into K segments. The resulting segmented image is losslessly compressed and transmitted to the decoder. The depth image is then compressed using a bi-modal block encoder, where the smooth blocks are predicted using direct spatial prediction. On the other hand, blocks containing depth discontinuities are approximated using a novel depth discontinuity predictor. The residual information is then compressed using a lossy compression strategy and transmitted to the receiver. Simulation results indicate that the proposed scheme outperforms the state of the art spatial video coding systems available today such as JPEG and H.264/AVC Intra. Moreover, the proposed scheme manages to outperform specialized depth image compression algorithms such as the one proposed by Zanuttigh and Cortelazzo.peer-reviewe

Exploiting color-depth image correlation to improve depth map compression

The multimedia signal processing community has recently identified the need to design depth map compression algorithms which preserve depth discontinuities in order to improve the rendering quality of virtual views for Free Viewpoint Video (FVV) services. This paper adopts contour detection with surround suppression on the color video to approximate the foreground edges present in the depth image. Displacement estimation and compensation is then used to improve this prediction and reduce the amount of side information required by the decoder. Simulation results indicate that the proposed method manages to accurately predict around 64% of the blocks. Moreover, the proposed scheme achieves a Peak Signal-to-Noise Ratio (PSNR) gain of around 4.9-6.6 dB relative to the JPEG standard and manages to outperform other state of the art depth map compression algorithms found in literature.peer-reviewe

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

WG1N5315 - Response to Call for AIC evaluation methodologies and compression technologies for medical images: LAR Codec

This document presents the LAR image codec as a response to Call for AIC evaluation methodologies and compression technologies for medical images.This document describes the IETR response to the specific call for contributions of medical imaging technologies to be considered for AIC. The philosophy behind our coder is not to outperform JPEG2000 in compression; our goal is to propose an open source, royalty free, alternative image coder with integrated services. While keeping the compression performances in the same range as JPEG2000 but with lower complexity, our coder also provides services such as scalability, cryptography, data hiding, lossy to lossless compression, region of interest, free region representation and coding

Compressão de geometria de pointcloud por decomposição booleana

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.PointClouds, ou nuvem de pontos, são um tipo de representação de uma cena tridimensional na qual cada ponto no espaço é definido por uma representação matricial de sua coordenada no eixo espacial xyz e sua cor por outra matriz RGB. Este tipo de arquitetura é extremamente interessante pela fácil manipulação. Porém, inevitavelmente, quanto mais detalhado o modelo tridimensional, maior o número de elementos no espaço e, consequentemente, maior o tamanho da informação necessária para descrever a PointCloud. Por este fato, se faz necessária a utilização de algoritmos de compressão para que seja possível utilizar menor espaço para o armazenamento ou transferência da informação. Este trabalho tem como objetivo, então, propor uma maneira de comprimir a geometria de PointClouds de forma eficaz e sem perdas a partir da decomposição do modelo tridimensional em cortes transversais e posterior compressão utilizando algoritmos já consolidados, como o JBIG, e manipulações booleanas. Em seguida, é realizada uma comparação entre o método proposto e métodos utilizados atualmente no mercado e os caracterizados como estado da arte. Os resultados são apresentados através de análises estatísticas da aplicação do algoritmo desenvolvido em uma grande base de dados de PointClouds, podendo representar de forma concisa os pontos positivos e negativos do projeto. O conteúdo pós análise consiste de reflexões a respeito dos prós e contras do método desenvolvido e propostas de futuro desenvolvimento.PointClouds are a type of three-dimensional representation of a scenery in which each volume element is described in the form of a three column matrix filled by each coordinate (x,y and z) value and its color by another matrix composed by its RGB values. This type of architecture is extremely interesting because of the easy manipulation it provides. However, inevitably, the more detail the PointCloud provides, the bigger the size of the information required to describe the PointCLoud. Due to this fact, it is necessary to use compressing algorithms to make these geometries easier to transfer or to store. This paper’s objective is to provide a reliable way to compress PointClouds’ geometries effectively and without losses by decomposing the model in its cross-sections and using existing binary image compression algorithms, such as JBIG, and Boolean manipulations to compress them. In addition, a comparison between algorithms currently used in the market and the state of art and the project is then made. The results are presented through a statistical analysis of the proposed method being applied in a large PointCloud data base, providing information about the positive and negative sides of the developed algorithm. The post analysis content is made of reflections about the pros and cons of the method and a series of ideas for future development

Livrable D4.2 of the PERSEE project : Représentation et codage 3D - Rapport intermédiaire - Définitions des softs et architecture

51Livrable D4.2 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 D4.2 du projet. Son titre : Représentation et codage 3D - Rapport intermédiaire - Définitions des softs et architectur