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COMPARISON OF SPARSE CODING AND JPEG CODING SCHEMES FOR BLURRED RETINAL IMAGES.
Title
COMPARISON OF SPARSE CODING AND JPEG CODING SCHEMES FOR BLURRED RETINAL IMAGES.
Creator
Chandrasekaran, Balaji, Wei, Lei, University of Central Florida
Abstract / Description
Overcomplete representations are currently one of the highly researched areas especially in the field of signal processing due to their strong potential to generate sparse representation of signals. Sparse representation implies that given signal can be represented with components that are only rarely significantly active. It has been strongly argued that the mammalian visual system is highly related towards sparse and overcomplete representations. The primary visual cortex has overcomplete...
Show moreOvercomplete representations are currently one of the highly researched areas especially in the field of signal processing due to their strong potential to generate sparse representation of signals. Sparse representation implies that given signal can be represented with components that are only rarely significantly active. It has been strongly argued that the mammalian visual system is highly related towards sparse and overcomplete representations. The primary visual cortex has overcomplete responses in representing an input signal which leads to the use of sparse neuronal activity for further processing. This work investigates the sparse coding with an overcomplete basis set representation which is believed to be the strategy employed by the mammalian visual system for efficient coding of natural images. This work analyzes the Sparse Code Learning algorithm in which the given image is represented by means of linear superposition of sparse statistically independent events on a set of overcomplete basis functions. This algorithm trains and adapts the overcomplete basis functions such as to represent any given image in terms of sparse structures. The second part of the work analyzes an inhibition based sparse coding model in which the Gabor based overcomplete representations are used to represent the image. It then applies an iterative inhibition algorithm based on competition between neighboring transform coefficients to select subset of Gabor functions such as to represent the given image with sparse set of coefficients. This work applies the developed models for the image compression applications and tests the achievable levels of compression of it. The research towards these areas so far proves that sparse coding algorithms are inefficient in representing high frequency sharp image features. So this work analyzes the performance of these algorithms only on the natural images which does not have sharp features and compares the compression results with the current industrial standard coding schemes such as JPEG and JPEG 2000. It also models the characteristics of an image falling on the retina after the distortion effects of the eye and then applies the developed algorithms towards these images and tests compression results.
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Date Issued
2007
Identifier
CFE0001701, ucf:47328
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001701
Applications of Compressive Sensing To Surveillance Problems
Title
Applications of Compressive Sensing To Surveillance Problems.
Creator
Huff, Christopher, Mohapatra, Ram, Sun, Qiyu, Han, Deguang, University of Central Florida
Abstract / Description
In many surveillance scenarios, one concern that arises is how to construct an imager that is capable of capturing the scene with high fidelity. This could be problematic for two reasons: first, the optics and electronics in the camera may have difficulty in dealing with so much information; secondly, bandwidth constraints, may pose difficulty in transmitting information from the imager to the user efficiently for reconstruction or realization. In this thesis, we will discuss a mathematical...
Show moreIn many surveillance scenarios, one concern that arises is how to construct an imager that is capable of capturing the scene with high fidelity. This could be problematic for two reasons: first, the optics and electronics in the camera may have difficulty in dealing with so much information; secondly, bandwidth constraints, may pose difficulty in transmitting information from the imager to the user efficiently for reconstruction or realization. In this thesis, we will discuss a mathematical framework that is capable of skirting the two aforementioned issues. This framework is rooted in a technique commonly referred to as compressive sensing. We will explore two of the seminal works in compressive sensing and will present the key theorems and definitions from these two papers. We will then survey three different surveillance scenarios and their respective compressive sensing solutions. The original contribution of this thesis is the development of a distributed compressive sensing model.
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Date Issued
2012
Identifier
CFE0004317, ucf:49473
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004317
Compressive Sensing and Recovery of Structured Sparse Signals
Title
Compressive Sensing and Recovery of Structured Sparse Signals.
Creator
Shahrasbi, Behzad, Rahnavard, Nazanin, Vosoughi, Azadeh, Wei, Lei, Atia, George, Pensky, Marianna, University of Central Florida
Abstract / Description
In the recent years, numerous disciplines including telecommunications, medical imaging, computational biology, and neuroscience benefited from increasing applications of high dimensional datasets. This calls for efficient ways of data capturing and data processing. Compressive sensing (CS), which is introduced as an efficient sampling (data capturing) method, is addressing this need. It is well-known that the signals, which belong to an ambient high-dimensional space, have much smaller...
Show moreIn the recent years, numerous disciplines including telecommunications, medical imaging, computational biology, and neuroscience benefited from increasing applications of high dimensional datasets. This calls for efficient ways of data capturing and data processing. Compressive sensing (CS), which is introduced as an efficient sampling (data capturing) method, is addressing this need. It is well-known that the signals, which belong to an ambient high-dimensional space, have much smaller dimensionality in an appropriate domain. CS taps into this principle and dramatically reduces the number of samples that is required to be captured to avoid any distortion in the information content of the data. This reduction in the required number of samples enables many new applications that were previously infeasible using classical sampling techniques.Most CS-based approaches take advantage of the inherent low-dimensionality in many datasets. They try to determine a sparse representation of the data, in an appropriately chosen basis using only a few significant elements. These approaches make no extra assumptions regarding possible relationships among the significant elements of that basis. In this dissertation, different ways of incorporating the knowledge about such relationships are integrated into the data sampling and the processing schemes.We first consider the recovery of temporally correlated sparse signals and show that using the time correlation model. The recovery performance can be significantly improved. Next, we modify the sampling process of sparse signals to incorporate the signal structure in a more efficient way. In the image processing application, we show that exploiting the structure information in both signal sampling and signal recovery improves the efficiency of the algorithm. In addition, we show that region-of-interest information can be included in the CS sampling and recovery steps to provide a much better quality for the region-of-interest area compared the rest of the image or video. In spectrum sensing applications, CS can dramatically improve the sensing efficiency by facilitating the coordination among spectrum sensors. A cluster-based spectrum sensing with coordination among spectrum sensors is proposed for geographically disperse cognitive radio networks. Further, CS has been exploited in this problem for simultaneous sensing and localization. Having access to this information dramatically facilitates the implementation of advanced communication technologies as required by 5G communication networks.
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Date Issued
2015
Identifier
CFE0006392, ucf:51509
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006392
REAL-TIME REALISTIC RENDERING AND HIGH DYNAMIC RANGE IMAGE DISPLAY AND COMPRESSION
Title
REAL-TIME REALISTIC RENDERING AND HIGH DYNAMIC RANGE IMAGE DISPLAY AND COMPRESSION.
Creator
Xu, Ruifeng, Pattanaik, Sumanta, University of Central Florida
Abstract / Description
This dissertation focuses on the many issues that arise from the visual rendering problem. Of primary consideration is light transport simulation, which is known to be computationally expensive. Monte Carlo methods represent a simple and general class of algorithms often used for light transport computation. Unfortunately, the images resulting from Monte Carlo approaches generally suffer from visually unacceptable noise artifacts. The result of any light transport simulation is, by its very...
Show moreThis dissertation focuses on the many issues that arise from the visual rendering problem. Of primary consideration is light transport simulation, which is known to be computationally expensive. Monte Carlo methods represent a simple and general class of algorithms often used for light transport computation. Unfortunately, the images resulting from Monte Carlo approaches generally suffer from visually unacceptable noise artifacts. The result of any light transport simulation is, by its very nature, an image of high dynamic range (HDR). This leads to the issues of the display of such images on conventional low dynamic range devices and the development of data compression algorithms to store and recover the corresponding large amounts of detail found in HDR images. This dissertation presents our contributions relevant to these issues. Our contributions to high dynamic range image processing include tone mapping and data compression algorithms. This research proposes and shows the efficacy of a novel level set based tone mapping method that preserves visual details in the display of high dynamic range images on low dynamic range display devices. The level set method is used to extract the high frequency information from HDR images. The details are then added to the range compressed low frequency information to reconstruct a visually accurate low dynamic range version of the image. Additional challenges associated with high dynamic range images include the requirements to reduce excessively large amounts of storage and transmission time. To alleviate these problems, this research presents two methods for efficient high dynamic range image data compression. One is based on the classical JPEG compression. It first converts the raw image into RGBE representation, and then sends the color base and common exponent to classical discrete cosine transform based compression and lossless compression, respectively. The other is based on the wavelet transformation. It first transforms the raw image data into the logarithmic domain, then quantizes the logarithmic data into the integer domain, and finally applies the wavelet based JPEG2000 encoder for entropy compression and bit stream truncation to meet the desired bit rate requirement. We believe that these and similar such contributions will make a wide application of high dynamic range images possible. The contributions to light transport simulation include Monte Carlo noise reduction, dynamic object rendering and complex scene rendering. Monte Carlo noise is an inescapable artifact in synthetic images rendered using stochastic algorithm. This dissertation proposes two noise reduction algorithms to obtain high quality synthetic images. The first one models the distribution of noise in the wavelet domain using a Laplacian function, and then suppresses the noise using a Bayesian method. The other extends the bilateral filtering method to reduce all types of Monte Carlo noise in a unified way. All our methods reduce Monte Carlo noise effectively. Rendering of dynamic objects adds more dimension to the expensive light transport simulation issue. This dissertation presents a pre-computation based method. It pre-computes the surface radiance for each basis lighting and animation key frame, and then renders the objects by synthesizing the pre-computed data in real-time. Realistic rendering of complex scenes is computationally expensive. This research proposes a novel 3D space subdivision method, which leads to a new rendering framework. The light is first distributed to each local region to form local light fields, which are then used to illuminate the local scenes. The method allows us to render complex scenes at interactive frame rates. Rendering has important applications in mixed reality. Consistent lighting and shadows between real scenes and virtual scenes are important features of visual integration. The dissertation proposes to render the virtual objects by irradiance rendering using live captured environmental lighting. This research also introduces a virtual shadow generation method that computes shadows cast by virtual objects to the real background. We finally conclude the dissertation by discussing a number of future directions for rendering research, and presenting our proposed approaches.
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Date Issued
2005
Identifier
CFE0000730, ucf:46615
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000730
A Psychophysical Approach to Standardizing Texture Compression for Virtual Environments
Title
A Psychophysical Approach to Standardizing Texture Compression for Virtual Environments.
Creator
Flynn, Jeremy, Szalma, James, Fidopiastis, Cali, Jentsch, Florian, Shah, Mubarak, University of Central Florida
Abstract / Description
Image compression is a technique to reduce overall data size, but its effects on human perception have not been clearly established. The purpose of this effort was to determine the most effective psychophysical method for subjective image quality assessment, and to apply those findings to an objective algorithm. This algorithm was used to identify the minimum level of texture compression noticeable to the human, in order to determine whether compression-induced texture distortion impacted...
Show moreImage compression is a technique to reduce overall data size, but its effects on human perception have not been clearly established. The purpose of this effort was to determine the most effective psychophysical method for subjective image quality assessment, and to apply those findings to an objective algorithm. This algorithm was used to identify the minimum level of texture compression noticeable to the human, in order to determine whether compression-induced texture distortion impacted game-play outcomes. Four experiments tested several hypotheses. The first hypothesis evaluated which of three magnitude estimation (ME) methods (absolute ME, absolute ME plus, or ME with a standard) for image quality assessment was the most reliable. The just noticeable difference (JND) point for textures compression against the Feature Similarity Index for color was determined The second hypothesis tested whether human participants perceived the same amount of distortion differently when textures were presented in three ways: when textures were displayed as flat images; when textures were wrapped around a model; and when textures were wrapped around models and in a virtual environment. The last set of hypotheses examined whether compression affected both subjective (immersion, technology acceptance, usability) and objective (performance) gameplay outcomes. The results were: the absolute magnitude estimation method was the most reliable; no difference was observed in the JND threshold between flat textures and textures placed on models, but textured embedded within the virtual environment were more noticeable than in the other two presentation formats. There were no differences in subjective gameplay outcomes when textures were compressed to below the JND thresholds; and those who played a game with uncompressed textures performed better on in-game tasks than those with the textures compressed, but only on the first in-game day. Practitioners and researchers can use these findings to guide their approaches to texture compression and experimental design.
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Date Issued
2018
Identifier
CFE0007178, ucf:52250
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007178