18th January 2010

SVD lossy adaptive encoding of 3-D digital images for ROI progressive transmission

Published paper “SVD lossy adaptive encoding of 3-D digital images for ROI progressive transmission” (I. Baeza, J.A. Verdoy, R.J. Villanueva, J. Villanueva-Oller), in the Image and Vision Computing nº 28, ISSN 0262-8859, pp. 449-457 (year 2010)

In this paper, we propose an algorithm for lossy adaptive encoding of digital three-dimensional (3D) images based on singular value decomposition (SVD). This encoding allows us to design algorithms for progressive transmission and reconstruction of the 3D image, for one or several selected regions of interest (ROI) avoiding redundancy in data transmission. The main characteristic of the proposed algorithms is that the ROIs can be selected during the transmission process and it is not necessary to re-encode the image again to transmit the data corresponding to the selected ROI. An example with a dataset of a CT scan consisting of 93 parallel slices where we added an implanted tumor (the ROI in this example) and a comparative with JPEG2000 are given.

Keywords: 3D digital images Singular value decomposition encoding, Lossy progressive transmission, Region of interest (ROI) transmission

posted in 3D, Investigación, JCR, Publicaciones, Reconstrucción de imagen, SVD - DVS, Transmisión progresiva | Comments Off on SVD lossy adaptive encoding of 3-D digital images for ROI progressive transmission

30th November 2009

ROI-based procedures for progressive transmission of digital images: A comparison

Published paper “ROI-based procedures for progressive transmission of digital images: A comparison” (I. Baeza, J.A. Verdoy, J. Villanueva-Oller, R.J. Villanueva), in the Mathematical and Computer Modelling nº 50, ISSN 0895-7177, pp. 849-859 (year 2009).

Nowadays, problems arise when handling big-sized images (i.e. medical image such as Computed Tomographies or satellite images) of 10, 50, 100 or more Megabytes, due to the amount of time required for transmiting and displaying, being this time even worse when a narrow bandwidth transmission media is involved (i.e. dial-up or mobile network), because receiver must wait until the entire image has arrived. To solve this issue, progressive transmission schemes are used. These schemes allow image sender to enconde the image data in such a way that it is possible for the receiver to perform a reconstruction of the original image since the very beginning of transmission. Despite this reconstruction being, of course, partial, it is possible to improve the reconstruction on the fly, as more and more information of the original image is received. There are many progressive transmission methods available, such as bit planes, TSVQ, DPCM, and, more recently, matrix polynomial interpolation, Discrete Cosine Transform (DCT, used in JPEG) and wavelets (used in JPEG2000). However, none of them are well suited, or perform poorly, when, in addition to progressive transmission, we want to include also ROIs (Region Of Interest) handling. In progressive transmission of ROIs, we want not only yo reconstruct the image as we receive image data, but also be able to select which part or parts of the emerging image we think are relevant and want to receive first, and which part or parts have no interest. In this context we present an algorithm for lossy adaptive encoding based on regular value decomposition (SVD). This algorithm turns out to be well suited for progressive transmission and ROI selection of 2D and 3D images, as it is able to avoid redundancy in data transmission and does not require any sort of data recodification, even if we select arbitrary ROIs on the fly.

Keywords: Adaptive progressive transmission, ROI, Region of Interest, reconstruction, singular value decomposition.

posted in 2D, 3D, Investigación, JCR, Publicaciones, Reconstrucción de imagen, SVD - DVS, Transmisión progresiva | Comments Off on ROI-based procedures for progressive transmission of digital images: A comparison

30th May 2006

SVD and matrix polynomial interpolation for lossy progressive transmission of 3D images

Published paper “SVD and matrix polynomial interpolation for lossy progressive transmission of 3D images” (I. Baeza, J.A. Verdoy, R.J. Villanueva, J. Villanueva-Oller, A.G. Law), in the book Computer Vision and Robotics, ISBN 1-59454-357-7, pp. 27-47 (year 2006). Ed. Nova Science Publishers.

This paper presents a new method for progressive transmission of 3D images that has four components: (1) decomposition of the image into regions using Singular Value Decomposition (SVD), (2) a reconstruction algorithm for progressive rendering that uses matrix polynomial interpolation along with approximations which are derived from SVD, (3) exploitation of a matrix norm for analyzing goodness of approximation, and (4) an optimal adaptive strategy for selecting “the next region to transmit”.

SVD of matrices is used in some areas of image processing, such as restoration, but not usually in transmission. For an image (matrix) of size m × n, its SVD produces an m ×m matrix, an n× n matrix, and a vector of size min {m, n}. That is, the SVD generates more than double the amount of original data. Despite this fact, however, a design of an appropriate adaptive transmission strategy within this four-component procedure provides an algorithm, for lossy progressive transmission, with excellent rendering and computational performance at low percentages of data transmission.

Keywords: Singular Value Decomposition, progressive image transmission, 3D images, progressive image reconstruction

posted in 3D, Investigación, Polinomios matriciales, Publicaciones, Reconstrucción de imagen, SVD - DVS, Transmisión progresiva | Comments Off on SVD and matrix polynomial interpolation for lossy progressive transmission of 3D images