Data compression opportunities in EOSDIS

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Planning/scheduling techniques for VQ-based image compression

The enormous size of the data holding and the complexity of the information system resulting from the EOS system pose several challenges to computer scientists, one of which is data archival and dissemination. More than ninety percent of the data holdings of NASA is in the form of images which will be accessed by users across the computer networks. Accessing the image data in its full resolution creates data traffic problems. Image browsing using a lossy compression reduces this data traffic, as well as storage by factor of 30-40. Of the several image compression techniques, VQ is most appropriate for this application since the decompression of the VQ compressed images is a table lookup process which makes minimal additional demands on the user's computational resources. Lossy compression of image data needs expert level knowledge in general and is not straightforward to use. This is especially true in the case of VQ. It involves the selection of appropriate codebooks for a given data set and vector dimensions for each compression ratio, etc. A planning and scheduling system is described for using the VQ compression technique in the data access and ingest of raw satellite data

1994 Science Information Management and Data Compression Workshop

This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on September 26-27, 1994, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival and retrieval of large quantities of data in future Earth and space science missions. It consisted of eleven presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center

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

The 1995 Science Information Management and Data Compression Workshop

This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center

Discrete wavelet transform based multispectral filter array demosaicking

International audienceThe idea of colour filter array may be adapted to multi-spectral image acquisition by integrating more filter types into the array, and developing associated demosaicking algorithms. Several methods employing discrete wavelet transform (DWT) have been proposed for CFA demosaicking. In this work, we put forward an extended use of DWT for mul-tispectral filter array demosaicking. The extension seemed straightforward, however we observed striking results. This work contributes to better understanding of the issue by demonstrating that spectral correlation and spatial resolution of the images exerts a crucial influence on the performance of DWT based demosaicking

Model-based VQ for image data archival, retrieval and distribution

An ideal image compression technique for image data archival, retrieval and distribution would be one with the asymmetrical computational requirements of Vector Quantization (VQ), but without the complications arising from VQ codebooks. Codebook generation and maintenance are stumbling blocks which have limited the use of VQ as a practical image compression algorithm. Model-based VQ (MVQ), a variant of VQ described here, has the computational properties of VQ but does not require explicit codebooks. The codebooks are internally generated using mean removed error and Human Visual System (HVS) models. The error model assumed is the Laplacian distribution with mean, lambda-computed from a sample of the input image. A Laplacian distribution with mean, lambda, is generated with uniform random number generator. These random numbers are grouped into vectors. These vectors are further conditioned to make them perceptually meaningful by filtering the DCT coefficients from each vector. The DCT coefficients are filtered by multiplying by a weight matrix that is found to be optimal for human perception. The inverse DCT is performed to produce the conditioned vectors for the codebook. The only image dependent parameter used in the generation of codebook is the mean, lambda, that is included in the coded file to repeat the codebook generation process for decoding

Use of remote sensing for land use policy formulation

Uses of remote sensing imagery were investigated based on exploring and evaluating the capability and reliability of all kinds of imagery for improving decision making on issues of land use at all scales of governmental administration. Emphasis was placed on applications to solving immediate problems confronting public agencies and private organizations. Resulting applications of remote sensing use by public agencies, public organizations, and related private corporations are described

Competitive Segmentation Performance on Near-lossless and Lossy Compressed Remote Sensing Images

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it impacts later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from sev- eral instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios. In some scenarios, accurate segmentation performance can be achieved even for high compression ratios