The image compression based on visual quality has got great interest over the past two decades. Meanwhile, it usually requires multi-resolution for the image code streams to browse images using electronical device. Recently, the image encoding respective to the above two factors become an attractive research area and gets great practical attractions in large image remote browsing scenarios, such as the pathology telemedicine. In this paper, we propose a visibility threshold model and an encoder based on measured HVS sensitivities and JPEG2000 standard. The proposed encoder can efficiently optimize the code stream on both HVS perception-based quality and display resolution. Moreover, the resulting code streams can be decoded with any JPEG2000 Part-I compliant decoder.
Recently, Han et. al. developed a method for visually lossless compression using JPEG2000. In this method, visibility thresholds (VTs) are experimentally measured and used during quantization to ensure that the errors introduced by quantization are below these thresholds. In this work, we extend the work of Han et. al. to visually lossy regime. We propose a framework where a series of experiments are conducted to measure Just-Noticeable-Differences using the quantization distortion model introduced by Han et. al. The resulting thresholds are incorporated into a JPEG2000 encoder to yield visually lossy, JPEG2000 Part 1 compliant codestreams.
In this paper, Compressive Sensing (CS) methods for Direct Sequence Spread Spectrum (DSSS) signals are
introduced. DSSS signals are formed by modulating the original signal by a Pseudo-Noise sequence. This
modulation spreads the spectra over a large bandwidth and makes interception of DSSS signals challenging.
Interception of DSSS signals using traditional methods require Analog-to-Digital Converters sampling at very
high rates to capture the full bandwidth. In this work, we propose CS methods that can intercept DSSS
signals from compressive measurements. The proposed methods are evaluated with DSSS signals generated
using Maximum-length Sequences and Binary Phase-Shift-Keying modulation at varying signal-to-noise and
compression ratios.
KEYWORDS: Signal detection, Sensors, Signal to noise ratio, Niobium, Receivers, Scanners, Modulation, Linear filtering, Monte Carlo methods, Interference (communication)
In this paper, compressive detection strategies for FHSS signals are introduced. Rapid switching of the carrier
frequency among many channels using a pseudorandom sequence makes detection of FHSS signals challenging.
The conventional approach to detect these signals is to rapidly scan small segments of the spectrum sequentially.
However, such a scanner has the inherent risk of never overlapping with the transmitted signal depending on
factors such as rate of hopping and scanning. In this paper, we propose compressive detection strategies that
sample the full spectrum in a compressive manner. Theory and simulations are presented to illustrate the benefits
of the proposed framework.
KEYWORDS: Compressed sensing, Signal to noise ratio, Modulation, Interference (communication), Associative arrays, Switching, Chromium, Frequency shift keying, Receivers, Signal processing
In this paper, compressive sensing strategies for interception of Frequency-Hopping Spread Spectrum (FHSS)
signals are introduced. Rapid switching of the carrier among many frequency channels using a pseudorandom
sequence (unknown to the eavesdropper) makes FHSS signals dicult to intercept. The conventional approach to
intercept FHSS signals necessitates capturing of all frequency channels and, thus, requires the Analog-to-Digital
Converters (ADCs) to sample at very high rates. Using the fact that the FHSS signals have sparse instanta-
neous spectra, we propose compressive sensing strategies for their interception. The proposed techniques are
validated using Gaussian Frequency-Shift Keying (GFSK) modulated FHSS signals as dened by the Bluetooth
specication.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.