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Adaptive vector-quantization scheme provides for rapid encoding of signals for transmission in compressed form and for rapid decoding at receiver. Based on simple heuristic "move-to-front" protocol effecting lossless compression of high-rate textual data. Audio, video, or other signals compressed efficiently.
The problem of electromagnetic field quantization is usually considered in textbooks under the assumption that the field occupies some empty box. The case when a nonuniform time-dependent dielectric medium is confined in some space region with time-dependent boundaries is studied. The basis of the subsequent consideration is the system of Maxwell's equations in linear passive time-dependent dielectric and magnetic medium without sources.
A pruning algorithm of Chou et al. (1989) for designing optimal tree structures identifies only those codebooks which lie on the convex hull of the original codebook's operational distortion rate function. The authors introduce a modified version of the original algorithm, which identifies a large number of codebooks having minimum average distortion, under the constraint that, in each step, only modes having no descendents are removed from the tree. All codebooks generated by the original algorithm are also generated by this algorithm. The new algorithm generates a much larger number of codebooks in the middle- and low-rate regions. The additional codebooks permit operation near the codebook's operational distortion rate function without time sharing by choosing from the increased number of available bit rates. Despite the statistical mismatch which occurs when coding data outside the training sequence, these pruned codebooks retain their performance advantage over full search vector quantizers (VQs) for a large range of rates.
Spectral vector quantization is presently applied to multispectral imagery from airborne scanners; the vector is here defined as an array of pixels from the same location from each channel. Attention is given to the effect of varying the size of the vector codebook on the quality of the compression and on subsequent classification. The rate of compression is programmable, but higher degradation is associated with higher compression ratios.
Volume rendering techniques typically process volumetric data in raw, uncompressed form. As algorithmic and architectural advances improve rendering speeds, however, larger data sets will be evaluated requiring consideration of data storage and transmission issues. In this paper, we analyze the data compression requirements for volume rendering applications and present a solution based on vector quantization. The proposed system compresses volumetric data and then renders images directly from the new data format. Tests on a fluid flow data set demonstrate that good image quality may be achieved at a compression ratio of 17:1 with only a 5 percent cost in additional rendering time.
Several refinements introduced to improve performance of data-compression scheme described in "Adaptive Vector-Quantization Scheme" (NPO-18186). Principal advantages of LAVQ are that complexity and coding time less than those of some other data-compression schemes. Also, does not require priori knowledge of either codebook or statistics of source data.
The residual vector quantization (RVQ) structure is exploited to produce a variable length codeword RVQ. Necessary conditions for the optimality of this RVQ are presented, and a new entropy-constrained RVQ (ECRVQ) design algorithm is shown to be very effective in designing RVQ codebooks over a wide range of bit rates and vector sizes. The new EC-RVQ has several important advantages. It can outperform entropy-constrained VQ (ECVQ) in terms of peak signal-to-noise ratio (PSNR), memory, and computation requirements. It can also be used to design high rate codebooks and codebooks with relatively large vector sizes. Experimental results indicate that when the new EC-RVQ is applied to image coding, very high quality is achieved at relatively low bit rates.
This paper describes the development of a digital correlator chip with the following features: 1 Giga-sample/second; 256 channels; 1-bit quantization; 32-bit counters providing up to 4 seconds integration time at 1 GHz; and very low power dissipation per channel. The improvements in the performance-to-cost ratio of the digital correlator chip are achieved with a combination of systolic architecture, novel pipelined differential logic circuits, and standard 1.0 micron CMOS process.
Recent studies of particle localization shows that square-integrable positive energy bispinor fields in a Minkowski space-time cannot be physically distinguished from constrained tensor fields. In this paper we generalize this result by characterizing all classical tensor systems, which admit Fermi quantization, as those having unitary Lie-Poisson brackets. Examples include Euler's tensor equation for a rigid body and Dirac's equation in tensor form.
We show that uncertainty relations, as well as minimum uncertainty coherent and squeezed states, are structural properties for diffusion processes. Through Nelson stochastic quantization we derive the stochastic image of the quantum mechanical coherent and squeezed states.
This paper describes the design and testing of an on-board SAR signal data compression algorithm for ESA's ENVISAT satellite. The Block Adaptive Quantization (BAQ) algorithm was selected, and optimized for the various operational modes of the ASAR instrument. A flexible BAQ scheme was developed which allows a selection of compression ratio/image quality trade-offs. Test results show the high quality of the SAR images processed from the reconstructed signal data, and the feasibility of on-board implementation using a single ASIC.
A progressive vector quantization (VQ) compression approach is discussed which decomposes image data into a number of levels using full search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIMD machine. We demonstrate progressive VQ on multispectral imagery obtained from the Advanced Very High Resolution Radiometer instrument and other Earth observation image data, and investigate the trade-offs in selecting the number of decomposition levels and codebook training method.
A system for data compression utilizing systolic array architecture for Vector Quantization (VQ) is disclosed for both full-searched and tree-searched. For a tree-searched VQ, the special case of a Binary Tree-Search VQ (BTSVQ) is disclosed with identical Processing Elements (PE) in the array for both a Raw-Codebook VQ (RCVQ) and a Difference-Codebook VQ (DCVQ) algorithm. A fault tolerant system is disclosed which allows a PE that has developed a fault to be bypassed in the array and replaced by a spare at the end of the array, with codebook memory assignment shifted one PE past the faulty PE of the array.
The long-term goal of this project is to study the nucleation of quantized vortices in helium II by investigating the behavior of rotating droplets of helium II in a reduced gravity environment. The objective of this ground-based research grant was to develop new experimental techniques to aid in accomplishing that goal. The development of an electrostatic levitator for superfluid helium, described below, and the successful suspension of charged superfluid drops in modest electric fields was the primary focus of this work. Other key technologies of general low temperature use were developed and are also discussed.
Pseudonoise (PN) code tracking loops in direct-sequence spread-spectrum systems are often implemented using digital hardware. Performance degradation due to quantization and sampling effects is not adequately characterized by the traditional analog system feedback loop analysis.
In previous papers we've shown how a well known data compression algorithm called Entropy-constrained Vector Quantization ( can be modified to reduce the size and complexity of very large, satellite data sets. In this paper, we descuss how to visualize and understand the content of such reduced data sets.
The temperature dependence of fluxoid quantization in a superconducting loop. The sensitivity of the device is expected to surpass that of other superconducting- based bolometric devices, such as superconducting transition-edge sensors and superconducting nanowire devices. Just as important, the proposed device has advantages in sample fabrication.
This method enables sensing and quantization of analog strain gauges. By manufacturing a piezoelectric sensor stack in parallel (physical) with a piezoelectric actuator stack, the capacitance of the sensor stack varies in exact proportion to the exertion applied by the actuator stack. This, in turn, varies the output frequency of the local sensor oscillator. The output, F(sub out), is fed to a phase detector, which is driven by a stable reference, F(sub ref). The output of the phase detector is a square waveform, D(sub out), whose duty cycle, t(sub W), varies in exact proportion according to whether F(sub out) is higher or lower than F(sub ref). In this design, should F(sub out) be precisely equal to F(sub ref), then the waveform has an exact 50/50 duty cycle. The waveform, D(sub out), is of generally very low frequency suitable for safe transmission over long distances without corruption. The active portion of the waveform, t(sub W), gates a remotely located counter, which is driven by a stable oscillator (source) of such frequency as to give sufficient digitization of t(sub W) to the resolution required by the application. The advantage to this scheme is that it negates the most-common, present method of sending either very low level signals (viz. direct output from the sensors) across great distances (anything over one-half meter) or the need to transmit widely varying higher frequencies over significant distances thereby eliminating interference [both in terms of beat frequency generation and in-situ EMI (electromagnetic interference)] caused by ineffective shielding. It also results in a significant reduction in shielding mass.