The need for compression is to minimize the storage space and reduction of transmission cost. When a digital image is transmitted through a communication channel, the cost of the transmission depends on the size of the data. The only way currently to improve on these resource requirements

The well known drawback of fractal image coding is its heavy computation complexity. In this paper, we show however that only a very small portion of this complexity is needed to encode the contours and the smooth areas of the objects of a certain minimum size. This is simply achieved by a local

Abstract—One of the most important goals of current and future sensor networks is energy-efficient communication of images. This paper presents a quantitative comparison between the energy costs associated with 1) direct transmission of uncompressed images and 2) sensor platform-based JPEG

, we proposed a protocol that uses wavelets to decorrelate sensor data, taking advantage of the compact support of the basis functions to keep costly inter-sensor communication at a minimum. In this paper, we prove an asymptotic optimality result for these protocols: the rate of growth for the traffic

Compressed Sensing (CS) is a novel mathematical framework that has revolutionized modern signal and image acquisition architectures ranging from one-pixel cameras, to range imaging and medical ultrasound imaging. According to CS, a sparse signal, or a signal that can be sparsely represented

of the spectrum by legacy sys-tems. Cognitive radio exhibits a tremendous promise for increasing the spectral efficiency for future wireless systems. Ideally, new secondary users would have a perfect panorama of the spectrum usage, and would opportunistically communicate over the available re-sources without

with limited computational power...). In this paper we introduce selective encryption from information theory point of view. We define a set of evaluation criteria for selective encryption algorithms and propose a novel selective encryption algorithm for JPEG2000 compressed images satisfying all these criteria

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order

April 29, 2011Für UnsShe had never before seen a rabbit with either a waistcoat-pocket, or a watch to take out of it, and burning with curiosity, she ran across the field after it Lewis CarrollThe following document, written under the supervision of Dr. reviewed by:

that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of time-varying data on a PC cluster.