2,107 research outputs found
Optimal sampling and quantization of synthetic aperture radar signals
Some theoretical and experimental results on optimal sampling and quantization of synthetic aperture radar (SAR) signals are presented. It includes a description of a derived theoretical relationship between the pixel signal to noise ratio of processed SAR images and the number of quantization bits per sampled signal, assuming homogeneous extended targets. With this relationship known, a solution may be realized for the problem of optimal allocation of a fixed data bit-volume (for specified surface area and resolution criterion) between the number of samples and the number of bits per sample. The results indicate that to achieve the best possible image quality for a fixed bit rate and a given resolution criterion, one should quantize individual samples coarsely and thereby maximize the number of multiple looks. The theoretical results are then compared with simulation results obtained by processing aircraft SAR data
A two-stage video coding framework with both self-adaptive redundant dictionary and adaptively orthonormalized DCT basis
In this work, we propose a two-stage video coding framework, as an extension
of our previous one-stage framework in [1]. The two-stage frameworks consists
two different dictionaries. Specifically, the first stage directly finds the
sparse representation of a block with a self-adaptive dictionary consisting of
all possible inter-prediction candidates by solving an L0-norm minimization
problem using an improved orthogonal matching pursuit with embedded
orthonormalization (eOMP) algorithm, and the second stage codes the residual
using DCT dictionary adaptively orthonormalized to the subspace spanned by the
first stage atoms. The transition of the first stage and the second stage is
determined based on both stages' quantization stepsizes and a threshold. We
further propose a complete context adaptive entropy coder to efficiently code
the locations and the coefficients of chosen first stage atoms. Simulation
results show that the proposed coder significantly improves the RD performance
over our previous one-stage coder. More importantly, the two-stage coder, using
a fixed block size and inter-prediction only, outperforms the H.264 coder
(x264) and is competitive with the HEVC reference coder (HM) over a large rate
range
Compression of interferometric radio-astronomical data
The volume of radio-astronomical data is a considerable burden in the
processing and storing of radio observations with high time and frequency
resolutions and large bandwidths. Lossy compression of interferometric
radio-astronomical data is considered to reduce the volume of visibility data
and to speed up processing.
A new compression technique named "Dysco" is introduced that consists of two
steps: a normalization step, in which grouped visibilities are normalized to
have a similar distribution; and a quantization and encoding step, which rounds
values to a given quantization scheme using a dithering scheme. Several
non-linear quantization schemes are tested and combined with different methods
for normalizing the data. Four data sets with observations from the LOFAR and
MWA telescopes are processed with different processing strategies and different
combinations of normalization and quantization. The effects of compression are
measured in image plane.
The noise added by the lossy compression technique acts like normal system
noise. The accuracy of Dysco is depending on the signal-to-noise ratio of the
data: noisy data can be compressed with a smaller loss of image quality. Data
with typical correlator time and frequency resolutions can be compressed by a
factor of 6.4 for LOFAR and 5.3 for MWA observations with less than 1% added
system noise. An implementation of the compression technique is released that
provides a Casacore storage manager and allows transparent encoding and
decoding. Encoding and decoding is faster than the read/write speed of typical
disks.
The technique can be used for LOFAR and MWA to reduce the archival space
requirements for storing observed data. Data from SKA-low will likely be
compressible by the same amount as LOFAR. The same technique can be used to
compress data from other telescopes, but a different bit-rate might be
required.Comment: Accepted for publication in A&A. 13 pages, 8 figures. Abstract was
abridge
Design of a digital compression technique for shuttle television
The determination of the performance and hardware complexity of data compression algorithms applicable to color television signals, were studied to assess the feasibility of digital compression techniques for shuttle communications applications. For return link communications, it is shown that a nonadaptive two dimensional DPCM technique compresses the bandwidth of field-sequential color TV to about 13 MBPS and requires less than 60 watts of secondary power. For forward link communications, a facsimile coding technique is recommended which provides high resolution slow scan television on a 144 KBPS channel. The onboard decoder requires about 19 watts of secondary power
Improved Modeling of the Correlation Between Continuous-Valued Sources in LDPC-Based DSC
Accurate modeling of the correlation between the sources plays a crucial role
in the efficiency of distributed source coding (DSC) systems. This correlation
is commonly modeled in the binary domain by using a single binary symmetric
channel (BSC), both for binary and continuous-valued sources. We show that
"one" BSC cannot accurately capture the correlation between continuous-valued
sources; a more accurate model requires "multiple" BSCs, as many as the number
of bits used to represent each sample. We incorporate this new model into the
DSC system that uses low-density parity-check (LDPC) codes for compression. The
standard Slepian-Wolf LDPC decoder requires a slight modification so that the
parameters of all BSCs are integrated in the log-likelihood ratios (LLRs).
Further, using an interleaver the data belonging to different bit-planes are
shuffled to introduce randomness in the binary domain. The new system has the
same complexity and delay as the standard one. Simulation results prove the
effectiveness of the proposed model and system.Comment: 5 Pages, 4 figures; presented at the Asilomar Conference on Signals,
Systems, and Computers, Pacific Grove, CA, November 201
Model for Estimation of Bounds in Digital Coding of Seabed Images
This paper proposes the novel model for estimation of bounds in digital coding of images. Entropy coding of images is exploited to measure the useful information content of the data. The bit rate achieved by reversible compression using the rate-distortion theory approach takes into account the contribution of the observation noise and the intrinsic information of hypothetical noise-free image. Assuming the Laplacian probability density function of the quantizer input signal, SQNR gains are calculated for image predictive coding system with non-adaptive quantizer for white and correlated noise, respectively. The proposed model is evaluated on seabed images. However, model presented in this paper can be applied to any signal with Laplacian distribution
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