Texture compression

Abstract: An image compression system wherein the image is arranged in a block 1 of elements which are Hadamard transformed 2 and then quantized 3 into sequences. The DC component is prediction 4 into a DC error prediction term and all the sequences are then encoded 5 into a variable length code depending on the combination of the sequences. The variable length codes are written into a buffer memory 6 at an input bit rate and read therefrom at an output bit rate asynchronously with the writing. A counter 10 keeps track of the amount of unused space in the buffer memory and a quantization characteristic selector 9 uses this amount to determine the quantization level.

Abstract: Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images.

Abstract: We present techniques for accelerated texture synthesis from example images. The key idea of our approach is to divide the task into two phases: analysis, and synthesis. During the analysis phase, which is performed once per sample texture, we generate a jump map. Using the jump map, the synthesis phase is capable of synthesizing texture similar to the analyzed example at interactive rates. We describe two such synthesis phase algorithms: one for creating images, and one for directly texturing manifold surfaces. We produce texture images at rates comparable to the fastest alternative algorithms, and produce textured surfaces an order of magnitude faster than current alternative approaches. We further develop a new, faster patch-based algorithm for image synthesis, which improves the quality of our results on ordered textures. We show how controls used for specifying texture synthesis on surfaces may be used on images as well, allowing interesting new image-based effects, and highlight modelling applications enabled by the speed of our approach.

Abstract: 4 Improved compression algorithm based on the Burrows–Wheeler transform 61 4.1 Modifications of the basic version of the compression algorithm. 61 5 Conclusions 141 iii Acknowledgements 145 Bibliography 147 Appendices 161 A Silesia corpus 163 B Implementation details 167 C Detailed options of examined compression programs 173 D Illustration of the properties of the weight functions 177 E Detailed compression results for files of different sizes and similar contents 185 List of Symbols and Abbreviations 191 List of Figures 195 List of Tables 198 Index 200 Chapter 1 Preface I am now going to begin my story (said the old man), so please attend. Contemporary computers process and store huge amounts of data. Some parts of these data are excessive. Data compression is a process that reduces the data size, removing the excessive information. Why is a shorter data sequence often more suitable? The answer is simple: it reduces the costs. A full-length movie of high quality could occupy a vast part of a hard disk. The compressed movie can be stored on a single CD-ROM. Large amounts of data are transmitted by telecommunication satellites. Without compression we would have to launch many more satellites that we do to transmit the same number of television programs. The capacity of Internet links is also limited and several methods reduce the immense amount of transmitted data. Some of them, as mirror or proxy servers, are solutions that minimise a number of transmissions on long distances. The other methods reduce the size of data by compressing them. Multimedia is a field in which data of vast sizes are processed. The sizes of text documents and application files also grow rapidly. Another type of data for which compression is useful are database tables. Nowadays, the amount of information stored in databases grows fast, while their contents often exhibit much redundancy. Data compression methods can be classified in several ways. One of the most important criteria of classification is whether the compression algorithm 1 2 CHAPTER 1. PREFACE removes some parts of data which cannot be recovered during the decompres-sion. The algorithms removing irreversibly some parts of data are called lossy, while others are called lossless. The lossy algorithms are usually used when a perfect consistency with the original data is not necessary after the decom-pression. Such a situation occurs for example in compression of video or picture data. If the recipient of the video …