Data Streams from the Low Frequency Instrument On-Board the Planck Satellite: Statistical Analysis and Compression Efficiency

The expected data rate produced by the Low Frequency Instrument (LFI) planned to fly on the ESA Planck mission in 2007, is over a factor 8 larger than the bandwidth allowed by the spacecraft transmission system to download the LFI data. We discuss the application of lossless compression to Planck/LFI data streams in order to reduce the overall data flow. We perform both theoretical analysis and experimental tests using realistically simulated data streams in order to fix the statistical properties of the signal and the maximal compression rate allowed by several lossless compression algorithms. We studied the influence of signal composition and of acquisition parameters on the compression rate Cr and develop a semiempirical formalism to account for it. The best performing compressor tested up to now is the arithmetic compression of order 1, designed for optimizing the compression of white noise like signals, which allows an overall compression rate = 2.65 +/- 0.02. We find that such result is not improved by other lossless compressors, being the signal almost white noise dominated. Lossless compression algorithms alone will not solve the bandwidth problem but needs to be combined with other techniques.Comment: May 3, 2000 release, 61 pages, 6 figures coded as eps, 9 tables (4 included as eps), LaTeX 2.09 + assms4.sty, style file included, submitted for the pubblication on PASP May 3, 200

SAR data compression: Application, requirements, and designs

The feasibility of reducing data volume and data rate is evaluated for the Earth Observing System (EOS) Synthetic Aperture Radar (SAR). All elements of data stream from the sensor downlink data stream to electronic delivery of browse data products are explored. The factors influencing design of a data compression system are analyzed, including the signal data characteristics, the image quality requirements, and the throughput requirements. The conclusion is that little or no reduction can be achieved in the raw signal data using traditional data compression techniques (e.g., vector quantization, adaptive discrete cosine transform) due to the induced phase errors in the output image. However, after image formation, a number of techniques are effective for data compression

Study and simulation of low rate video coding schemes

The semiannual report is included. Topics covered include communication, information science, data compression, remote sensing, color mapped images, robust coding scheme for packet video, recursively indexed differential pulse code modulation, image compression technique for use on token ring networks, and joint source/channel coder design

MICCS: A Novel Framework for Medical Image Compression Using Compressive Sensing

The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-of-Interest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio

Digital mammography, cancer screening: Factors important for image compression

The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers

Optimization of Planck/LFI on--board data handling

To asses stability against 1/f noise, the Low Frequency Instrument (LFI) onboard the Planck mission will acquire data at a rate much higher than the data rate allowed by its telemetry bandwith of 35.5 kbps. The data are processed by an onboard pipeline, followed onground by a reversing step. This paper illustrates the LFI scientific onboard processing to fit the allowed datarate. This is a lossy process tuned by using a set of 5 parameters Naver, r1, r2, q, O for each of the 44 LFI detectors. The paper quantifies the level of distortion introduced by the onboard processing, EpsilonQ, as a function of these parameters. It describes the method of optimizing the onboard processing chain. The tuning procedure is based on a optimization algorithm applied to unprocessed and uncompressed raw data provided either by simulations, prelaunch tests or data taken from LFI operating in diagnostic mode. All the needed optimization steps are performed by an automated tool, OCA2, which ends with optimized parameters and produces a set of statistical indicators, among them the compression rate Cr and EpsilonQ. For Planck/LFI the requirements are Cr = 2.4 and EpsilonQ <= 10% of the rms of the instrumental white noise. To speedup the process an analytical model is developed that is able to extract most of the relevant information on EpsilonQ and Cr as a function of the signal statistics and the processing parameters. This model will be of interest for the instrument data analysis. The method was applied during ground tests when the instrument was operating in conditions representative of flight. Optimized parameters were obtained and the performance has been verified, the required data rate of 35.5 Kbps has been achieved while keeping EpsilonQ at a level of 3.8% of white noise rms well within the requirements.Comment: 51 pages, 13 fig.s, 3 tables, pdflatex, needs JINST.csl, graphicx, txfonts, rotating; Issue 1.0 10 nov 2009; Sub. to JINST 23Jun09, Accepted 10Nov09, Pub.: 29Dec09; This is a preprint, not the final versio

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