Dimension reduction of image and audio space

The reduction of data necessary for storage or transmission is a desirable goal in the digital video and audio domain. Compression schemes strive to reduce the amount of storage space or bandwidth necessary to keep or move the data. Data reduction can be accomplished so that visually or audibly unnecessary data is removed or recoded thus aiding the compression phase of the data processing. The characterization and identification of data that can be successfully removed or reduced is the purpose of this work. New philosophy, theory and methods for data processing are presented towards the goal of data reduction. The philosophy and theory developed in this work establish a foundation for high speed data reduction suitable for multi-media applications. The developed methods encompass motion detection and edge detection as features of the systems. The philosophy of energy flow analysis in video processing enables the consideration of noise in digital video data. Research into noise versus motion leads to an efficient and successful method of identifying motion in a sequence. The research of the underlying statistical properties of vector quantization provides an insight into the performance characteristics of vector quantization and leads to successful improvements in application. The underlying statistical properties of the vector quantization process are analyzed and three theorems are developed and proved. The theorems establish the statistical distributions and probability densities of various metrics of the vector quantization process. From these properties, an intelligent and efficient algorithm design is developed and tested. The performance improvements in both time and quality are established through algorithm analysis and empirical testing. The empirical results are presented

High efficiency block coding techniques for image data.

by Lo Kwok-tung.Thesis (Ph.D.)--Chinese University of Hong Kong, 1992.Includes bibliographical references.ABSTRACT --- p.iACKNOWLEDGEMENTS --- p.iiiLIST OF PRINCIPLE SYMBOLS AND ABBREVIATIONS --- p.ivLIST OF FIGURES --- p.viiLIST OF TABLES --- p.ixTABLE OF CONTENTS --- p.xChapter CHAPTER 1 --- IntroductionChapter 1.1 --- Background - The Need for Image Compression --- p.1-1Chapter 1.2 --- Image Compression - An Overview --- p.1-2Chapter 1.2.1 --- Predictive Coding - DPCM --- p.1-3Chapter 1.2.2 --- Sub-band Coding --- p.1-5Chapter 1.2.3 --- Transform Coding --- p.1-6Chapter 1.2.4 --- Vector Quantization --- p.1-8Chapter 1.2.5 --- Block Truncation Coding --- p.1-10Chapter 1.3 --- Block Based Image Coding Techniques --- p.1-11Chapter 1.4 --- Goal of the Work --- p.1-13Chapter 1.5 --- Organization of the Thesis --- p.1-14Chapter CHAPTER 2 --- Block-Based Image Coding TechniquesChapter 2.1 --- Statistical Model of Image --- p.2-1Chapter 2.1.1 --- One-Dimensional Model --- p.2-1Chapter 2.1.2 --- Two-Dimensional Model --- p.2-2Chapter 2.2 --- Image Fidelity Criteria --- p.2-3Chapter 2.2.1 --- Objective Fidelity --- p.2-3Chapter 2.2.2 --- Subjective Fidelity --- p.2-5Chapter 2.3 --- Transform Coding Theroy --- p.2-6Chapter 2.3.1 --- Transformation --- p.2-6Chapter 2.3.2 --- Quantization --- p.2-10Chapter 2.3.3 --- Coding --- p.2-12Chapter 2.3.4 --- JPEG International Standard --- p.2-14Chapter 2.4 --- Vector Quantization Theory --- p.2-18Chapter 2.4.1 --- Codebook Design and the LBG Clustering Algorithm --- p.2-20Chapter 2.5 --- Block Truncation Coding Theory --- p.2-22Chapter 2.5.1 --- Optimal MSE Block Truncation Coding --- p.2-24Chapter CHAPTER 3 --- Development of New Orthogonal TransformsChapter 3.1 --- Introduction --- p.3-1Chapter 3.2 --- Weighted Cosine Transform --- p.3-4Chapter 3.2.1 --- Development of the WCT --- p.3-6Chapter 3.2.2 --- Determination of a and β --- p.3-9Chapter 3.3 --- Simplified Cosine Transform --- p.3-10Chapter 3.3.1 --- Development of the SCT --- p.3-11Chapter 3.4 --- Fast Computational Algorithms --- p.3-14Chapter 3.4.1 --- Weighted Cosine Transform --- p.3-14Chapter 3.4.2 --- Simplified Cosine Transform --- p.3-18Chapter 3.4.3 --- Computational Requirement --- p.3-19Chapter 3.5 --- Performance Evaluation --- p.3-21Chapter 3.5.1 --- Evaluation using Statistical Model --- p.3-21Chapter 3.5.2 --- Evaluation using Real Images --- p.3-28Chapter 3.6 --- Concluding Remarks --- p.3-31Chapter 3.7 --- Note on Publications --- p.3-32Chapter CHAPTER 4 --- Pruning in Transform Coding of ImagesChapter 4.1 --- Introduction --- p.4-1Chapter 4.2 --- "Direct Fast Algorithms for DCT, WCT and SCT" --- p.4-3Chapter 4.2.1 --- Discrete Cosine Transform --- p.4-3Chapter 4.2.2 --- Weighted Cosine Transform --- p.4-7Chapter 4.2.3 --- Simplified Cosine Transform --- p.4-9Chapter 4.3 --- Pruning in Direct Fast Algorithms --- p.4-10Chapter 4.3.1 --- Discrete Cosine Transform --- p.4-10Chapter 4.3.2 --- Weighted Cosine Transform --- p.4-13Chapter 4.3.3 --- Simplified Cosine Transform --- p.4-15Chapter 4.4 --- Operations Saved by Using Pruning --- p.4-17Chapter 4.4.1 --- Discrete Cosine Transform --- p.4-17Chapter 4.4.2 --- Weighted Cosine Transform --- p.4-21Chapter 4.4.3 --- Simplified Cosine Transform --- p.4-23Chapter 4.4.4 --- Generalization Pruning Algorithm for DCT --- p.4-25Chapter 4.5 --- Concluding Remarks --- p.4-26Chapter 4.6 --- Note on Publications --- p.4-27Chapter CHAPTER 5 --- Efficient Encoding of DC Coefficient in Transform Coding SystemsChapter 5.1 --- Introduction --- p.5-1Chapter 5.2 --- Minimum Edge Difference (MED) Predictor --- p.5-3Chapter 5.3 --- Performance Evaluation --- p.5-6Chapter 5.4 --- Simulation Results --- p.5-9Chapter 5.5 --- Concluding Remarks --- p.5-14Chapter 5.6 --- Note on Publications --- p.5-14Chapter CHAPTER 6 --- Efficient Encoding Algorithms for Vector Quantization of ImagesChapter 6.1 --- Introduction --- p.6-1Chapter 6.2 --- Sub-Codebook Searching Algorithm (SCS) --- p.6-4Chapter 6.2.1 --- Formation of the Sub-codebook --- p.6-6Chapter 6.2.2 --- Premature Exit Conditions in the Searching Process --- p.6-8Chapter 6.2.3 --- Sub-Codebook Searching Algorithm --- p.6-11Chapter 6.3 --- Predictive Sub-Codebook Searching Algorithm (PSCS) --- p.6-13Chapter 6.4 --- Simulation Results --- p.6-17Chapter 6.5 --- Concluding Remarks --- p.5-20Chapter 6.6 --- Note on Publications --- p.6-21Chapter CHAPTER 7 --- Predictive Classified Address Vector Quantization of ImagesChapter 7.1 --- Introduction --- p.7-1Chapter 7.2 --- Optimal Three-Level Block Truncation Coding --- p.7-3Chapter 7.3 --- Predictive Classified Address Vector Quantization --- p.7-5Chapter 7.3.1 --- Classification of Images using Three-level BTC --- p.7-6Chapter 7.3.2 --- Predictive Mean Removal Technique --- p.7-8Chapter 7.3.3 --- Simplified Address VQ Technique --- p.7-9Chapter 7.3.4 --- Encoding Process of PCAVQ --- p.7-13Chapter 7.4 --- Simulation Results --- p.7-14Chapter 7.5 --- Concluding Remarks --- p.7-18Chapter 7.6 --- Note on Publications --- p.7-18Chapter CHAPTER 8 --- Recapitulation and Topics for Future InvestigationChapter 8.1 --- Recapitulation --- p.8-1Chapter 8.2 --- Topics for Future Investigation --- p.8-3REFERENCES --- p.R-1APPENDICESChapter A. --- Statistics of Monochrome Test Images --- p.A-lChapter B. --- Statistics of Color Test Images --- p.A-2Chapter C. --- Fortran Program Listing for the Pruned Fast DCT Algorithm --- p.A-3Chapter D. --- Training Set Images for Building the Codebook of Standard VQ Scheme --- p.A-5Chapter E. --- List of Publications --- p.A-

Scalable Nearest Neighbor Search with Compact Codes

An important characteristic of the recent decade is the dramatic growth in the use and generation of data. From collections of images, documents and videos, to genetic data, and to network traffic statistics, modern technologies and cheap storage have made it possible to accumulate huge datasets. But how can we effectively use all this data? The growing sizes of the modern datasets make it crucial to develop new algorithms and tools capable of sifting through this data efficiently. A central computational primitive for analyzing large datasets is the Nearest Neighbor Search problem in which the goal is to preprocess a set of objects, so that later, given a query object, one can find the data object closest to the query. In most situations involving high-dimensional objects, the exhaustive search which compares the query with every item in the dataset has a prohibitive cost both for runtime and memory space. This thesis focuses on the design of algorithms and tools for fast and cost efficient nearest neighbor search. The proposed techniques advocate the use of compressed and discrete codes for representing the neighborhood structure of data in a compact way. Transforming high-dimensional items, such as raw images, into similarity-preserving compact codes has both computational and storage advantages as compact codes can be stored efficiently using only a few bits per data item, and more importantly they can be compared extremely fast using bit-wise or look-up table operators. Motivated by this view, the present work explores two main research directions: 1) finding mappings that better preserve the given notion of similarity while keeping the codes as compressed as possible, and 2) building efficient data structures that support non-exhaustive search among the compact codes. Our large-scale experimental results reported on various benchmarks including datasets upto one billion items, show boost in retrieval performance in comparison to the state-of-the-art

Comparison of CELP speech coder with a wavelet method

This thesis compares the speech quality of Code Excited Linear Predictor (CELP, Federal Standard 1016) speech coder with a new wavelet method to compress speech. The performances of both are compared by performing subjective listening tests. The test signals used are clean signals (i.e. with no background noise), speech signals with room noise and speech signals with artificial noise added. Results indicate that for clean signals and signals with predominantly voiced components the CELP standard performs better than the wavelet method but for signals with room noise the wavelet method performs much better than the CELP. For signals with artificial noise added, the results are mixed depending on the level of artificial noise added with CELP performing better for low level noise added signals and the wavelet method performing better for higher noise levels

Advanced wireless communications using large numbers of transmit antennas and receive nodes

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. First, we propose practical open-loop and closed-loop training frameworks to reduce the overhead of the downlink training phase. We then discuss efficient CSI quantization techniques using a trellis search. The proposed CSI quantization techniques can be implemented with a complexity that only grows linearly with the number of transmit antennas while the performance is close to the optimal case. We also analyze distributed reception using a large number of geographically separated nodes, a scenario that may become popular with the emergence of the Internet of Things. For distributed reception, we first propose coded distributed diversity to minimize the symbol error probability at the fusion center when the transmitter is equipped with a single antenna. Then we develop efficient receivers at the fusion center using minimal processing overhead at the receive nodes when the transmitter with multiple transmit antennas sends multiple symbols simultaneously using spatial multiplexing