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A survey of current data compression techniques which are being used to reduce the amount of data in remote sensing applications is provided. The survey aspect is far from complete, reflecting the substantial activity in this area. The purpose of the survey is more to exemplify the different approaches being taken rather than to provide an exhaustive list of the various proposed approaches.
Synchronization and error correcting codes effect on data compression
Image data compression system performance prediction and evaluation in presence of noise
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.
NASA continuous communications systems growth has increased the demand for image transmission and storage. Research and analysis was conducted on various lossy and lossless advanced data compression techniques or approaches used to improve the efficiency of transmission and storage of high volume stellite image data such as pulse code modulation (PCM), differential PCM (DPCM), transform coding, hybrid coding, interframe coding, and adaptive technique. In this presentation, the fundamentals of image data compression utilizing two techniques which are pulse code modulation (PCM) and differential PCM (DPCM) are presented along with an application utilizing these two coding techniques.
A general approach to compression of diverse data from large scientific projects has been developed and this paper addresses the appropriate system and scientific constraints together with the algorithm development and test strategy. This framework has been implemented for the COsmic Background Explorer spacecraft (COBE) by retrofitting the existing VAS-based data management system with high-performance compression software permitting random access to the data. Algorithms which incorporate scientific knowledge and consume relatively few system resources are preferred over ad hoc methods. COBE exceeded its planned storage by a large and growing factor and the retrieval of data significantly affects the processing, delaying the availability of data for scientific usage and software test. Embedded compression software is planned to make the project tractable by reducing the data storage volume to an acceptable level during normal processing.
Data compression with error-control coding for space telemetry
This paper reports on the integration of a lossless data compression/decompression chipset into a space data system architecture. For its compression engine, the data system incorporates the Universal Source Encoder (USE) designed for the NASA/Goddard Space Flight Center. Currently, the data compression testbed generates video frames consisting of 512 lines of 512 pixels having 8-bit resolution. Each image is passed through the USE where the lines are internally partitioned into 16-word blocks. These blocks are adaptively encoded across widely varying entropy levels using a Rice 12-option set coding algorithm. The current system operates at an Input/Output rate of 10 Msamples/s or 80 Mbits/s for each buffered input line. Frame and line synchronization for each image are maintained through the use of uniquely decodable command words. Length information of each variable length compressed image line is also included in the output stream. The data and command information are passed to the next stage of the system architecture through a serial fiber-optic transmitter. The initial segment of this stage consists of packetizer hardware which adds an appropriate CCSDS header to the received source data. An uncompressed mode is optionally available to pass image lines directly to the packetizer hardware. A data decompression testbed has also been developed to confirm the data compression operation.
Functional description of the real-time programmable data compression system that has been developed for operation between the data acquisition and satellite service ground stations and the ATS 1 and 3 satellites. The source data consist of TV pictures from these satellites that are received, compressed, transmitted, and reconstructed in real-time. General purpose minicomputers and specially designed I/O channels are used for compression and reconstruction. The installed compression system has passed all system input-output tests. Because of the great flexibility of the minicomputers, it is possible to vary the compression and to perform auxiliary tasks, such as data formatting.
The potential of image data compression techniques to satisfy the anticipated requirements of imaging spectrometer missions is discussed. Noiseless coding, rate controlled compression, cluster compression, and error protection are addressed.
The commercial JPEG standard complies well with the specific requirements of exploratory space missions. Therefore, JPEG has been chosen to be the baseline for a series of spaceborne image data compressions (e.g. MARS94-HRSC, -WAOSS, HUYGENS-DISR, MESUR-IMP). One S/W-implementation (IMP) and one H/W-implementation (DISR) of image data compression are presented. Details of the modifications applied to standard JPEG are outlined. Finally, a performance comparison of the two implementations is given.
A high performance lossy data compression technique is currently being developed for space science applications under the requirement of high-speed push-broom scanning. The technique is also error-resilient in that error propagation is contained within a few scan lines. The algorithm is based on block-transform combined with bit-plane encoding; this combination results in an embedded bit string with exactly the desirable compression rate. The lossy coder is described. The compression scheme performs well on a suite of test images typical of images from spacecraft instruments. Hardware implementations are in development; a functional chip set is expected by the end of 2001.
The Consultative Committee for Space Data Systems (CCSDS) has been engaging in recommending data compression standards for space applications. The first effort focused on a lossless scheme that was adopted in 1997. Since then, space missions benefiting from this recommendation range from deep space probes to near Earth observatories. The cost savings result not only from reduced onboard storage and reduced bandwidth, but also in ground archive of mission data. In many instances, this recommendation also enables more science data to be collected for added scientific value. Since 1998, the compression sub-panel of CCSDS has been investigating lossy image compression schemes and is currently working towards a common solution for a single recommendation. The recommendation will fulfill the requirements for remote sensing conducted on space platforms.
The popular JPEG image compression standard is an example of a block transform-based compression scheme; the image is systematically subdivided into block that are individually transformed, quantized, and encoded. The compression is achieved by quantizing the transformed data, reducing the data entropy and thus facilitating efficient encoding. A generic block transform model is introduced.
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A case study is presented where an image segmentation based compression technique is applied to LANDSAT Thematic Mapper (TM) and Nimbus-7 Coastal Zone Color Scanner (CZCS) data. The compression technique, called Spatially Constrained Clustering (SCC), can be regarded as an adaptive vector quantization approach. The SCC can be applied to either single or multiple spectral bands of image data. The segmented image resulting from SCC is encoded in small rectangular blocks, with the codebook varying from block to block. Lossless compression potential (LDP) of sample TM and CZCS images are evaluated. For the TM test image, the LCP is 2.79. For the CZCS test image the LCP is 1.89, even though when only a cloud-free section of the image is considered the LCP increases to 3.48. Examples of compressed images are shown at several compression ratios ranging from 4 to 15. In the case of TM data, the compressed data are classified using the Bayes' classifier. The results show an improvement in the similarity between the classification results and ground truth when compressed data are used, thus showing that compression is, in fact, a useful first step in the analysis.
The amount of data collected from spaceborne remote sensing has substantially increased in the last years. During same time period, the ability to store or transmit data has not increased as quickly. At this time, there is a growing interest in developing compression schemes that could provide both higher compression ratios and lower encoding/decoding errors. In the case of the spaceborne Synthetic Aperture Radar (SAR) earth observation system developed by the French Space Agency (CNES), the volume of data to be processed will exceed both the on-board storage capacities and the telecommunication link. The objective of this paper is twofold: to present various compression schemes adapted to SAR data; and to define a set of evaluation criteria and compare the algorithms on SAR data. In this paper, we review two classical methods of SAR data compression and propose novel approaches based on Fourier Transforms and spectrum coding.
Data compression techniques have been investigated for use with modal analysis applications. A redundancy-reduction algorithm was used to compress frequency response functions (FRFs) in order to reduce the amount of disk space necessary to store the data and/or save time in processing it. Tests were performed for both single- and multiple-degree-of-freedom (SDOF and MDOF, respectively) systems, with varying amounts of noise. Analysis was done on both the compressed and uncompressed FRFs using an SDOF Nyquist curve fit as well as the Eigensystem Realization Algorithm. Significant savings were realized with minimal errors incurred by the compression process.