Background 1.2. Overview of image quality measures 1.3. Description and organization of thesis 2. HVS MODEL 2.1. Brightness perception 2.2. Contrast sensitivity function 12 2.3. Simple HVS model 3. IMAGE QUALITY MEASURE 3.1. MSE as image quality measure 3.2. Definition of image quality measure 29 4. RESULTS 4.1. "Lena" image with various types of distortion 4.2. "Goldhill" image with various types of distortion 37 4.3. "Couple" image with various types of distortion 42 4.4. "Tiffany", "Lake"and "Mandrill" images with JPEG coding 4.5. "Woman", "Man" and "Barbara" images with blurring 4.6. "Lena" image with inverted grayscale 4.7. "Lena" image with blurring and additive noise 57 4.8. Comparison of JPEG and JPEG2000 coding using "Lena" image 4.9. Performance of quality measure on two different images 70 5. CONCLUSION 71 REFERENCES
Abstract-We consider the problem of RF signal analysis where one sensing node observes a frequency band possibly used by multiple packet based radio transmitters. Analysis of the received signal consists of two steps. In the first step we use spectrogram to perform temporal segmentation of the received piecewise statistically homogeneous signal. This task is formulated as a clustering problem. In the second step we compute a certain 2-D slice of the fourth order spectrum for each of the segments found in the first step. The fourth order spectrum slices are arranged in a three-way array. Key idea of the second step is to use uniqueness properties of the low rank decomposition of the three-way array to recover spectra and associated activity sequences of individual components in the received signal. We derive a numerical algorithm for the low rank decomposition, which computes estimates of the spectra and activity sequences by optimizing a weighted least squares criterion under application specific constraints. The approach is illustrated with simulation examples involving signals used in 802.11a/b/g and Bluetooth networks. The proposed algorithm can be used as a spectrum analysis tool, providing crucial information needed for achieving efficient utilization of radio spectrum and elimination of mutual interference between the coexisting systems.
We consider the scenario where one or more sensors observe a frequency band potentially used by multiple radio transmitters forming packet based networks. Our goal is to develop algorithms for estimation of spectrum usage in space, time, and frequency. This estimation is obtained by performing some form of analysis of the received signals at the sensors. The proposed algorithms can be used for achieving efficient spectrum utilization by identifying unused portions of spectrum in space, time and frequency as well as for other applications requiring spectrum monitoring.The received signals consist of packets from multiple transmitters with possible timefrequency collisions. Each received signal consists of multiple statistically homogeneous segments where each combination of active transmitted signals creates one or more of such segments. In order to perform any form of statistical analysis using conventional methods for stationary or cyclostationary signals these segments must be first localized in time. In the first part of the thesis we propose a nonparametric algorithm for solving this problem. Initial segmentation is computed using a variant of mean shift algorithm, which is a clustering tool based on nonparametric estimate of the underlying probability distribution. We show that this type of mean shift algorithm is based on the modified Newton's method and provide a convergence analysis which explains how and why ii the algorithm works. Final segmentation results are obtained after applying a cluster validation procedure and impulse noise filtering on the initial segmentation results.In the second part of the thesis we propose a method for analysis of the segments localized in the first step. This method is useful if transmitted signals are linearly modulated or can be approximated as sums of linearly modulated signals. For each set of segments generated by the same combination of the transmitted signals we compute a certain two dimensional slice of the fourth order spectrum. These slices are arranged in a three way array. We show that under certain conditions it is possible to recover contributions of individual signals to the observed three way array by decomposing the array into low rank terms. Thus, for each received signal we can estimate its spectrum and the associated activity sequence in time. We discuss the uniqueness conditions, treat the nontrivial problem of fourth order spectrum estimation and propose a numerical algorithm for estimation of the spectra and the associated activity sequences of individual signals from the observed three way array.The algorithms for segmentation and fourth order spectrum based analysis require only one sensor. In the third part of the thesis we assume that multiple sensors are available. Using the algorithms mentioned above for each transmitter we can estimate its received spectrum at different sensors. From the received spectra of the same transmitted signal at different sensors it is possible to estimate the source signal spectrum and transfer functions of the ch...
An improved algorithm for image quality assessment is presented. First a simple model of human visual system, consisting of a nonlinear function and a 2-D filter, processes the input images. This filter has one user-defined parameter, whose value depends on the reference image. This way the algorithm can adapt to different scenarios. In the next step the average value of locally computed correlation coefficients between the two processed images is found. This criterion is closely related to the way in which human observer assesses image quality. Finally, image quality measure is computed as the average value of locally computed correlation coefficients, adjusted by the average correlation coefficient between the reference and error images. By this approach the proposed measure differentiates between the random and signal dependant distortions, which have different effects on human observer. Performance of the proposed quality measure is illustrated by examples involving images with different types of degradation.
We consider a scenario with multiple radio sources performing packet based transmissions. The sources belong to heterogeneous networks and their signals may overlap in time and frequency. Each source is characterized by its power spectral density and an on/off activity sequence. A network of sensors performs measurements, where each sensor computes spectrogram of the received signal with certain time and frequency resolution. Spectrograms from different sensors are collected and arraigned in a three-way array, whose three dimensions correspond to space, time, and frequency indices. We show that, under certain rank conditions of the three-way array, it is possible to recover sources to sensors channel gain coefficients, power spectral densities and on/off activity sequences of the sources by decomposing the three-way array into rank-one components. The recovery process is illustrated with simulation examples involving 802.11b/g and Bluetooth sources whose signals overlap in time and frequency. I. INTRODUCTIONIn this work we consider a scenario with multiple radio sources operating in the same frequency band. Such a scenario happens in the 2.4 GHz ISM band which is used by WLAN radios, Bluetooth devices, cordless phones, Zig-bee radios, microwave ovens etc. The sources from different systems operate in this frequency band without any coordination and hence, they transmit signals that may overlap in time and frequency. These signals are received by a network of sensors. Each radio source is characterized by its power spectral density(PSD) and on/off activity in time. In order to capture this nonstationary behavior, each sensor computes spectrogram with certain time and frequency resolution. The spectrograms from different locations are collected and processed using a centralized scheme. Collected spectrograms form a three-way array, whose three dimensions correspond to space, time, and frequency indices. The key observation is that under certain rank conditions, sources to sensors channel gain coefficients, PSDs and on/off activity sequences of the sources can be recovered uniquely by decomposing the received 3-way array into rank-one components [1]. In general, the decomposition process happens in two steps. In the first step we perform block decomposition, which separates heterogeneous groups of sources. The block decomposition requires a non-trivial generalization of the basic uniqueness condition [2]. In the second step we recover individual contributions of sources within each group(rank-one components) by assuming nonoverlapping transmissions in time. This step reduces to a clustering problem. The proposed algorithm can identify multiple interfering signals, which may overlap in time and frequency.
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