3,351 research outputs found
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
Iterative Quantization Using Codes On Graphs
We study codes on graphs combined with an iterative message passing algorithm
for quantization. Specifically, we consider the binary erasure quantization
(BEQ) problem which is the dual of the binary erasure channel (BEC) coding
problem. We show that duals of capacity achieving codes for the BEC yield codes
which approach the minimum possible rate for the BEQ. In contrast, low density
parity check codes cannot achieve the minimum rate unless their density grows
at least logarithmically with block length. Furthermore, we show that duals of
efficient iterative decoding algorithms for the BEC yield efficient encoding
algorithms for the BEQ. Hence our results suggest that graphical models may
yield near optimal codes in source coding as well as in channel coding and that
duality plays a key role in such constructions.Comment: 10 page
Content-Aware Quantization Index Modulation:Leveraging Data Statistics for Enhanced Image Watermarking
Image watermarking techniques have continuously evolved to address new
challenges and incorporate advanced features. The advent of data-driven
approaches has enabled the processing and analysis of large volumes of data,
extracting valuable insights and patterns. In this paper, we propose two
content-aware quantization index modulation (QIM) algorithms: Content-Aware QIM
(CA-QIM) and Content-Aware Minimum Distortion QIM (CAMD-QIM). These algorithms
aim to improve the embedding distortion of QIM-based watermarking schemes by
considering the statistics of the cover signal vectors and messages. CA-QIM
introduces a canonical labeling approach, where the closest coset to each cover
vector is determined during the embedding process. An adjacency matrix is
constructed to capture the relationships between the cover vectors and
messages. CAMD-QIM extends the concept of minimum distortion (MD) principle to
content-aware QIM. Instead of quantizing the carriers to lattice points,
CAMD-QIM quantizes them to close points in the correct decoding region.
Canonical labeling is also employed in CAMD-QIM to enhance its performance.
Simulation results demonstrate the effectiveness of CA-QIM and CAMD-QIM in
reducing embedding distortion compared to traditional QIM. The combination of
canonical labeling and the minimum distortion principle proves to be powerful,
minimizing the need for changes to most cover vectors/carriers. These
content-aware QIM algorithms provide improved performance and robustness for
watermarking applications.Comment: 12 pages, 10 figure
A Tight Bound on the Performance of a Minimal-Delay Joint Source-Channel Coding Scheme
An analog source is to be transmitted across a Gaussian channel in more than
one channel use per source symbol. This paper derives a lower bound on the
asymptotic mean squared error for a strategy that consists of repeatedly
quantizing the source, transmitting the quantizer outputs in the first channel
uses, and sending the remaining quantization error uncoded in the last channel
use. The bound coincides with the performance achieved by a suboptimal decoder
studied by the authors in a previous paper, thereby establishing that the bound
is tight.Comment: 5 pages, submitted to IEEE International Symposium on Information
Theory (ISIT) 201
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