Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard
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Ian Blanes↗
SEARCH · Engineering Papers
Results for “image compression”
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An image compression survey and algorithm switching based on scene activity
Data compression techniques are presented. A description of these techniques is provided along with a performance evaluation. The complexity of the hardware resulting from their implementation is also addressed. The compression effect on channel distortion and the applicability of these algorithms to real-time processing are presented. Also included is a proposed new direction for an adaptive compression technique for real-time processing.
Hart, M. M.↗
Parallel image compression
A parallel compression algorithm for the 16,384 processor MPP machine was developed. The serial version of the algorithm can be viewed as a combination of on-line dynamic lossless test compression techniques (which employ simple learning strategies) and vector quantization. These concepts are described. How these concepts are combined to form a new strategy for performing dynamic on-line lossy compression is discussed. Finally, the implementation of this algorithm in a massively parallel fashion on the MPP is discussed.
Reif, John H.↗
Integer cosine transform for image compression
This article describes a recently introduced transform algorithm called the integer cosine transform (ICT), which is used in transform-based data compression schemes. The ICT algorithm requires only integer operations on small integers and at the same time gives a rate-distortion performance comparable to that offered by the floating-point discrete cosine transform (DCT). The article addresses the issue of implementation complexity, which is of prime concern for source coding applications of interest in deep-space communications. Complexity reduction in the transform stage of the compression scheme is particularly relevant, since this stage accounts for most (typically over 80 percent) of the computational load.
Cheung, K.-M.↗
The Effect of Lossy Ultrasound Image Compression on the Precision of Carotid Wall Thickmess Measurements
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ultrasound IMT data compession carotid wall↗
Method and apparatus for optical encoding with compressible imaging
The present invention presents an optical encoder with increased conversion rates. Improvement in the conversion rate is a result of combining changes in the pattern recognition encoder's scale pattern with an image sensor readout technique which takes full advantage of those changes, and lends itself to operation by modern, high-speed, ultra-compact microprocessors and digital signal processors (DSP) or field programmable gate array (FPGA) logic elements which can process encoder scale images at the highest speeds. Through these improvements, all three components of conversion time (reciprocal conversion rate)--namely exposure time, image readout time, and image processing time--are minimized.
Leviton, Douglas B.↗
The New CCSDS Standard for Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression
This paper describes the emerging Issue 2 of the CCSDS-123.0-B standard for low-complexity compression of multispectral and hyperspectral imagery, focusing on its new features and capabilities. Most significantly, this new issue incorporates a closed-loop quantization scheme to provide near-lossless compression capability while still supporting lossless compression, and introduces a new entropy coding option that provides better compression of low-entropy data.
Kiely, Aaron B.↗
Image Compression: Sparse Coding vs. Bottleneck Autoencoders
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Computer Science↗
An adaptive technique to maximize lossless image data compression of satellite images
Data compression will pay an increasingly important role in the storage and transmission of image data within NASA science programs as the Earth Observing System comes into operation. It is important that the science data be preserved at the fidelity the instrument and the satellite communication systems were designed to produce. Lossless compression must therefore be applied, at least, to archive the processed instrument data. In this paper, we present an analysis of the performance of lossless compression techniques and develop an adaptive approach which applied image remapping, feature-based image segmentation to determine regions of similar entropy and high-order arithmetic coding to obtain significant improvements over the use of conventional compression techniques alone. Image remapping is used to transform the original image into a lower entropy state. Several techniques were tested on satellite images including differential pulse code modulation, bi-linear interpolation, and block-based linear predictive coding. The results of these experiments are discussed and trade-offs between computation requirements and entropy reductions are used to identify the optimum approach for a variety of satellite images. Further entropy reduction can be achieved by segmenting the image based on local entropy properties then applying a coding technique which maximizes compression for the region. Experimental results are presented showing the effect of different coding techniques for regions of different entropy. A rule-base is developed through which the technique giving the best compression is selected. The paper concludes that maximum compression can be achieved cost effectively and at acceptable performance rates with a combination of techniques which are selected based on image contextual information.
Stewart, Robert J.↗
Image data compression application to imaging spectrometers
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.
Rice, R. F.↗
High-performance compression of astronomical images
Astronomical images have some rather unusual characteristics that make many existing image compression techniques either ineffective or inapplicable. A typical image consists of a nearly flat background sprinkled with point sources and occasional extended sources. The images are often noisy, so that lossless compression does not work very well; furthermore, the images are usually subjected to stringent quantitative analysis, so any lossy compression method must be proven not to discard useful information, but must instead discard only the noise. Finally, the images can be extremely large. For example, the Space Telescope Science Institute has digitized photographic plates covering the entire sky, generating 1500 images each having 14000 x 14000 16-bit pixels. Several astronomical groups are now constructing cameras with mosaics of large CCD's (each 2048 x 2048 or larger); these instruments will be used in projects that generate data at a rate exceeding 100 MBytes every 5 minutes for many years. An effective technique for image compression may be based on the H-transform (Fritze et al. 1977). The method that we have developed can be used for either lossless or lossy compression. The digitized sky survey images can be compressed by at least a factor of 10 with no noticeable losses in the astrometric and photometric properties of the compressed images. The method has been designed to be computationally efficient: compression or decompression of a 512 x 512 image requires only 4 seconds on a Sun SPARCstation 1. The algorithm uses only integer arithmetic, so it is completely reversible in its lossless mode, and it could easily be implemented in hardware for space applications.
White, Richard L.↗
Experimental Studies on a Compact Storage Scheme for Wavelet-based Multiresolution Subregion Retrieval
Wavelet transforms, when combined with quantization and a suitable encoding, can be used to compress images effectively. In order to use them for image library systems, a compact storage scheme for quantized coefficient wavelet data must be developed with a support for fast subregion retrieval. We have designed such a scheme and in this paper we provide experimental studies to demonstrate that it achieves good image compression ratios, while providing a natural indexing mechanism that facilitates fast retrieval of portions of the image at various resolutions.
Poulakidas, A.↗
Collected Data of The Boreal Ecosystem and Atmosphere Study (BOREAS)
The Boreal Ecosystem-Atmosphere Study (BOREAS) was a large-scale international interdisciplinary climate-ecosystem interaction experiment in the northern boreal forests of Canada. Its goal was to improve our understanding of the boreal forests -- how they interact with the atmosphere, how much CO2 they can store, and how climate change will affect them. BOREAS wanted to learn to use satellite data to monitor the forests, and to improve computer simulation and weather models so scientists can anticipate the effects of global change. This BOREAS CD-ROM set is a set of 12 CD-ROMs containing the finalized point data sets and compressed image data from the BOREAS Project. All point data are stored in ASCII text files, and all image and GIS products are stored as binary images, compressed using GZip. Additional descriptions of the various data sets on this CD-ROM are available in other documents in the BOREAS series.
Newcomer, J.↗
Smoothing DCT Compression Artifacts
Image compression based on quantizing the image in the discrete cosine transform (DCT) domain can generate blocky artifacts in the output image. It is possible to reduce these artifacts and RMS error by adjusting measures of block edginess and image roughness, while restricting the DCT coefficient values to values that would have been quantized to those of the compressed image. We also introduce a DCT coefficient amplitude adjustment that reduces RMS error.
Ahumada, A. J., Jr.↗
Image data compression having minimum perceptual error
A method for performing image compression that eliminates redundant and invisible image components is described. The image compression uses a Discrete Cosine Transform (DCT) and each DCT coefficient yielded by the transform is quantized by an entry in a quantization matrix which determines the perceived image quality and the bit rate of the image being compressed. The present invention adapts or customizes the quantization matrix to the image being compressed. The quantization matrix comprises visual masking by luminance and contrast techniques and by an error pooling technique all resulting in a minimum perceptual error for any given bit rate, or minimum bit rate for a given perceptual error.
Watson, Andrew B.↗
Image Data Compression Having Minimum Perceptual Error
A method is presented for performing color or grayscale image compression that eliminates redundant and invisible image components. The image compression uses a Discrete Cosine Transform (DCT) and each DCT coefficient yielded by the transform is quantized by an entry in a quantization matrix which determines the perceived image quality and the bit rate of the image being compressed. The quantization matrix comprises visual masking by luminance and contrast technique all resulting in a minimum perceptual error for any given bit rate, or minimum bit rate for a given perceptual error.
Watson, Andrew B.↗
Progressive Transmission and Compression of Images
We describe an image data compression strategy featuring progressive transmission. The method exploits subband coding and arithmetic coding for compression. We analyze the Laplacian probability density, which closely approximates the statistics of individual subbands, to determine a strategy for ordering the compressed subband data in a way that improves rate-distortion performance. Results are presented for a test image.
Kiely, A. B.↗
Adaptive Bandwidth Compression For Moving Images
Data compressed in images with fast motion and expanded when motion slows. Proposed data-compression scheme adapts transmission rate for moving image to speed of motion of image. Laser rangefinder measures speed of target. Adaptive sampler determines required resolution of target image on basis of measured speed and provides decommutator at control station with more or fewer partial image fields per unit time. When monitor displays maximum number of fields simultaneously, resolution is highest.
Graham, Olin L.↗