Thesis (M.S.)--Rochester Institute of Technology, 2005. / Typescript. Includes bibliographical references (leaves 112-116).

Thesis (MEng)--Stellenbosch University, 2000. / ENGLISH ABSTRACT: The purpose of this thesis is to investigate the nature of digital image compression and the calculation of the quality of the compressed images. The work is focused on greyscale images in the domain of satellite images and aerial photographs. Two compression techniques are studied in detail namely the JPEG and fractal compression methods. Implementations of both these techniques are then applied to a set of test images. The rest of this thesis is dedicated to investigating the measurement of the loss of quality that was introduced by the compression. A general method for quality measurement (signal To Noise Ratio) is discussed as well as a technique that was presented in literature quite recently (Grey Block Distance). Hereafter, a new measure is presented. After this, a means of comparing the performance of these measures is presented. It was found that the new measure for image quality estimation performed marginally better than the SNR algorithm. Lastly, some possible improvements on this technique are mentioned and the validity of the method used for comparing the quality measures is discussed. / AFRIKAANSE OPSOMMING: Die doel van hierdie tesis is om ondersoek in te stel na die aard van digitale beeldsamepersing en die berekening van beeldkwaliteit na samepersing. Daar word gekonsentreer op grysvlak beelde in die spesifieke domein van satellietbeelde en lugfotos. Twee spesifieke samepersingstegnieke word in diepte ondersoek naamlik die JPEG en fraktale samepersingsmetodes. Implementasies van beide hierdie tegnieke word op 'n stel toetsbeelde aangewend. Die res van hierdie tesis word dan gewy aan die ondersoek van die meting van die kwaliteitsverlies van hierdie saamgeperste beelde. Daar word gekyk na 'n metode wat in algemene gebruik in die praktyk is asook na 'n nuwer metode wat onlangs in die literatuur veskyn het. Hierna word 'n nuwe tegniek bekendgestel. Verder word daar 'n vergelyking van hierdie mates en 'n ondersoek na die interpretasie van die 'kwaliteit' van hierdie kwaliteitsmate gedoen. Daar is gevind dat die nuwe maatstaf vir kwaliteit net so goed en selfs beter werk as die algemene maat vir beeldkwaliteit naamlik die Sein tot Ruis Verhouding. Laastens word daar moontlike verbeterings op die maatstaf genoem en daar volg 'n bespreking oor die geldigheid van die metode wat gevolg is om die kwaliteit van die kwaliteitsmate te bepaal

This thesis describes the design of fast JPEG processing accelerators for a DSP processor. Certain computation tasks are moved from the DSP processor to hardware accelerators. The accelerators are slave co processing machines and are controlled via a new instruction set. The clock cycle and power consumption is reduced by utilizing the custom built hardware. The hardware can perform the tasks in fewer clock cycles and several tasks can run in parallel. This will reduce the total number of clock cycles needed. First a decoder and an encoder were implemented in DSP assembler. The cycle consumption of the parts was measured and from this the hardware/software partitioning was done. Behavioral models of the accelerators were then written in C++ and the assembly code was modified to work with the new hardware. Finally, the accelerators were implemented using Verilog. Extension of the accelerator instructions was given following a custom design flow.

Due to limited hardware, effectively using the available resources is crucial for mobile games. Texture compression is a fundamental technique in game development to reduce the demand of memory and bandwidth usage. This thesis evaluates JPEG, PVRTC and uncompressed PVR with emphasis on loading time, memory footprint, application size and visual quality. The goal of this case study is to find the most suitable compression technique for a specific game. A variant of uncompressed PVR, RGBA4444, was found to be the best technique to use in this case. It was also concluded that JPEG compression in general is a bad choice for games due to the lack of an alpha channel. Severe visual artifacts were noticed on frame-by-frame animation when using PVRTC. It is therefore interesting to investigate other animation techniques, such as skeletal animation, in combination with texture compression, to avoid such artifacts.

Image sizes have increased exponentially in recent years. The resulting high-resolution images are often viewed via remote image browsing. Zooming and panning are desirable features in this context, which result in disparate spatial regions of an image being displayed at a variety of ( spatial) resolutions. When an image is displayed at a reduced resolution, the quantization step sizes needed for visually lossless quality generally increase. This paper investigates the quantization step sizes needed for visually lossless display as a function of resolution, and proposes a method that effectively incorporates the resulting ( multiple) quantization step sizes into a single JPEG 2000 codestream. This codestream is JPEG 2000 Part 1 compliant and allows for visually lossless decoding at all resolutions natively supported by the wavelet transform as well as arbitrary intermediate resolutions, using only a fraction of the full-resolution codestream. When images are browsed remotely using the JPEG 2000 Interactive Protocol ( JPIP), the required bandwidth is significantly reduced, as demonstrated by extensive experimental results.

This thesis deals with the management of digital photos by metadata contained in the photos. The thesis describes the structure of the formats JFIF, TIFF, RAW and EXIF format for storing metadata into photos. In the next part of this thesis is described the design and implementation of a simple photo management application. The main functionality of the application is focused on bulk editing EXIF metadata in the photos. In the conclusion of this thesis there's a proof of results and discussion about further extension options.

Background: With the boom in the internet space and social media platforms, a large number of images are being shared. With this rise and advancements in technology, many image editing tools have made their way to giving rise to digital image manipulation. Being able to differentiate a forged image is vital to avoid misinformation or misrepresentation. This study focuses on the splicing image forgery to localizes the forged region in the tampered image. Objectives: The main purpose of the thesis is to extend the capability of the JPEG Ghost model by localizing the tampering in the image. This is done by analyzing the difference curves formed by compressions in the tampered image, and thereafter comparing the performance of the models. Methods: The study is carried out by two research methods; one being a Literature Review, whose main goal is gaining insights on the existing studies in terms of the approaches and techniques followed; and the second being Experiment; whose main goal is to improve the JPEG ghost algorithm by localizing the forged area in a tampered image and to compare three machine learning models based on the performance metrics. The machine learning models that are compared are Random Forest, XGBoost, and Support Vector Machine. Results: The performance of the above-mentioned models has been compared with each other on the same dataset. Results from the experiment showed that XGBoost had the best overall performance over other models with the Jaccard Index value of 79.8%. Conclusions: The research revolves around localization of the forged region in a tampered image using the concept of JPEG ghosts. This is We have concluded that the performance of XGBoost model is the best, followed by Random Forest and then Support Vector Machine.

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The amount of information that we are dealing with today is being generated at an ever-increasing rate. On one hand, data compression is needed to efficiently store, organize the data and transport the data over the limited-bandwidth network. On the other hand, efficient information retrieval is needed to speedily find the relevant information from this huge mass of data using available resources. The compressed pattern matching problem can be stated as: given the compressed format of a text or an image and a pattern string or a pattern image, report the occurrence(s) of the pattern in the text or image with minimal (or no) decompression. The main advantages of compressed pattern matching versus the naïve decompress-then-search approach are: First, reduced storage cost. Since there is no need to decompress the data or there is only minimal decompression required, the disk space and the memory cost is reduced. Second, less search time. Since the size of the compressed data is smaller than that of the original data, a searching performed on the compressed data will result in a shorter search time. The challenge of efficient compressed pattern matching can be met from two inseparable aspects: First, to utilize effectively the full potential of compression for the information retrieval systems, there is a need to develop search-aware compression algorithms. Second, for data that is compressed using a particular compression technique, regardless whether the compression is search-aware or not, we need to develop efficient searching techniques. This means that techniques must be developed to search the compressed data with no or minimal decompression and with not too much extra cost. Compressed pattern matching algorithms can be categorized as either for text compression or for image compression. Although compressed pattern matching for text compression has been studied for a few years and many publications are available in the literature, there is still room to improve the efficiency in terms of both compression and searching. None of the search engines available today make explicit use of compressed pattern matching. Compressed pattern matching for image compression, on the other hand, has been relatively unexplored. However, it is getting more attention because lossless compression has become more important for the ever-increasing large amount of medical images, satellite images and aerospace photos, which requires the data to be losslessly stored. Developing efficient information retrieval techniques from the losslessly compressed data is therefore a fundamental research challenge. In this dissertation, we have studied compressed pattern matching problem for both text and images. We present a series of novel compressed pattern matching algorithms, which are divided into two major parts. The first major work is done for the popular LZW compression algorithm. The second major work is done for the current lossless image compression standard JPEG-LS. Specifically, our contributions from the first major work are: 1. We have developed an "almost-optimal" compressed pattern matching algorithm that reports all pattern occurrences. An earlier "almost-optimal" algorithm reported in the literature is only capable of detecting the first occurrence of the pattern and the practical performance of the algorithm is not clear. We have implemented our algorithm and provide extensive experimental results measuring the speed of our algorithm. We also developed a faster implementation for so-called "simple patterns". The simple patterns are patterns that no unique symbol appears more than once. The algorithm takes advantage of this property and runs in optimal time. 2. We have developed a novel compressed pattern matching algorithm for multiple patterns using the Aho-Corasick algorithm. The algorithm takes O(mt+n+r) time with O(mt) extra space, where n is the size of the compressed file, m is the total size of all patterns, t is the size of the LZW trie and r is the number of occurrences of the patterns. The algorithm is particularly efficient when being applied on archival search if the archives are compressed with a common LZW trie. All the above algorithms have been implemented and extensive experiments have been conducted to test the performance of our algorithms and to compare with the best existing algorithms. The experimental results show that our compressed pattern matching algorithm for multiple patterns is competitive among the best algorithms and is practically the fastest among all approaches when the number of patterns is not very large. Therefore, our algorithm is preferable for general string matching applications. LZW is one of the most efficient and popular compression algorithms used extensively and both of our algorithms require no modification on the compression algorithm. Our work, therefore, has great economical and market potential Our contributions from the second major work are: 1 We have developed a new global context variation of the JPEG-LS compression algorithm and the corresponding compressed pattern matching algorithm. Comparing to the original JPEG-LS, the global context variation is search-aware and has faster encoding and decoding speeds. The searching algorithm based on the global-context variation requires partial decompression of the compressed image. The experimental results show that it improves the search speed by about 30% comparing to the decompress-then-search approach. Based on our best knowledge, this is the first two-dimensional compressed pattern matching work for the JPEG-LS standard. 2 We have developed a two-pass variation of the JPEG-LS algorithm and the corresponding compressed pattern matching algorithm. The two-pass variation achieves search-awareness through a common compression technique called semi-static dictionary. Comparing to the original algorithm, the compression of the new algorithm is equally well but the encoding takes slightly longer. The searching algorithm based on the two-pass variation requires no decompression at all and therefore works in the fully compressed domain. It runs in time O(nc+mc+nm+m^2) with extra space O(n+m+mc), where n is the number of columns of the image, m is the number of rows and columns of the pattern, nc is the compressed image size and mc is the compressed pattern size. The algorithm is the first known two-dimensional algorithm that works in the fully compressed domain.

Les algorithmes de compression d'images JPEG et JPEG2000 sont présentés, puis comparés grâce à une métrique perceptuelle. L'algorithme JPEG décompose une image par la transformée en cosinus discrète, approxime les coefficients transformés par une quantisation uniforme et encode le résultat par l'algorithme de Huffman. Pour l'algorithme JPEG2000, on utilise une transformée en ondelettes décomposant une image en plusieurs résolutions. On décrit et justifie la construction d'ondelettes orthogonales ou biorthogonales ayant le maximum de propriétés parmi les suivantes: valeurs réelles, support compact, plusieurs moments, régularité et symétrie. Ensuite, on explique sommairement le fonctionnement de l'algorithme JPEG2000, puis on montre que la métrique RMSE n'est pas bonne pour mesurer l'erreur perceptuelle. On présente donc quelques idées pour la construction d'une métrique perceptuelle se basant sur le fonctionnement du système de vision humain, décrivant en particulier la métrique SSIM. On utilise finalement cette dernière métrique pour conclure que JPEG2000 fait mieux que JPEG. / In the present work we describe the image compression algorithms: JPEG and JPEG2000. We then compare them using a perceptual metric. JPEG algorithm decomposes an image with the discrete cosine transform, the transformed map is then quantized and encoded with the Huffman code. Whereas the JPEG2000 algorithm uses wavelet transform to decompose an image in many resolutions. We describe a few properties of wavelets and prove their utility in image compression. The wavelets properties are for instance: orthogonality or biorthogonality, real wavelets, compact support, number of moments, regularity and symmetry. We then briefly show how does JPEG2000 work. After we prove that RMSE error is clearly not the best perceptual metric. So forth we suggest other metrics based on a human vision system model. We describe the SSIM index and suggest it as a tool to evaluate image quality. Finally, using the SSIM metric, we show that JPEG2000 surpasses JPEG.