Exclusive-or preprocessing and dictionary coding of continuous-tone images.

The field of lossless image compression studies the various ways to represent image data in the most compact and efficient manner possible that also allows the image to be reproduced without any loss. One of the most efficient strategies used in lossless compression is to introduce entropy reduction through decorrelation. This study focuses on using the exclusive-or logic operator in a decorrelation filter as the preprocessing phase of lossless image compression of continuous-tone images. The exclusive-or logic operator is simply and reversibly applied to continuous-tone images for the purpose of extracting differences between neighboring pixels. Implementation of the exclusive-or operator also does not introduce data expansion. Traditional as well as innovative prediction methods are included for the creation of inputs for the exclusive-or logic based decorrelation filter. The results of the filter are then encoded by a variation of the Lempel-Ziv-Welch dictionary coder. Dictionary coding is selected for the coding phase of the algorithm because it does not require the storage of code tables or probabilities and because it is lower in complexity than other popular options such as Huffman or Arithmetic coding. The first modification of the Lempel-Ziv-Welch dictionary coder is that image data can be read in a sequence that is linear, 2-dimensional, or an adaptive combination of both. The second modification of the dictionary coder is that the coder can instead include multiple, dynamically chosen dictionaries. Experiments indicate that the exclusive-or operator based decorrelation filter when combined with a modified Lempel-Ziv-Welch dictionary coder provides compression comparable to algorithms that represent the current standard in lossless compression. The proposed algorithm provides compression performance that is below the Context-Based, Adaptive, Lossless Image Compression (CALIC) algorithm by 23%, below the Low Complexity Lossless Compression for Images (LOCO-I) algorithm by 19%, and below the Portable Network Graphics implementation of the Deflate algorithm by 7%, but above the Zip implementation of the Deflate algorithm by 24%. The proposed algorithm uses the exclusive-or operator in the modeling phase and uses modified Lempel-Ziv-Welch dictionary coding in the coding phase to form a low complexity, reversible, and dynamic method of lossless image compression

Empirical analysis of BWT-based lossless image compression

The Burrows-Wheeler Transformation (BWT) is a text transformation algorithm originally designed to improve the coherence in text data. This coherence can be exploited by compression algorithms such as run-length encoding or arithmetic coding. However, there is still a debate on its performance on images. Motivated by a theoretical analysis of the performance of BWT and MTF, we perform a detailed empirical study on the role of MTF in compressing images with the BWT. This research studies the compression performance of BWT on digital images using different predictors and context partitions. The major interest of the research is in finding efficient ways to make BWT suitable for lossless image compression.;This research studied three different approaches to improve the compression of image data by BWT. First, the idea of preprocessing the image data before sending it to the BWT compression scheme is studied by using different mapping and prediction schemes. Second, different variations of MTF were investigated to see which one works best for Image compression with BWT. Third, the concept of context partitioning for BWT output before it is forwarded to the next stage in the compression scheme.;For lossless image compression, this thesis proposes the removal of the MTF stage from the BWT compression pipeline and the usage of context partitioning method. The compression performance is further improved by using MED predictor on the image data along with the 8-bit mapping of the prediction residuals before it is processed by BWT.;This thesis proposes two schemes for BWT-based image coding, namely BLIC and BLICx, the later being based on the context-ordering property of the BWT. Our methods outperformed other text compression algorithms such as PPM, GZIP, direct BWT, and WinZip in compressing images. Final results showed that our methods performed better than the state of the art lossless image compression algorithms, such as JPEG-LS, JPEG2000, CALIC, EDP and PPAM on the natural images

Adaptive edge-based prediction for lossless image compression

Many lossless image compression methods have been suggested with established results hard to surpass. However there are some aspects that can be considered to improve the performance further. This research focuses on two-phase prediction-encoding method, separately studying each and suggesting new techniques.;In the prediction module, proposed Edge-Based-Predictor (EBP) and Least-Squares-Edge-Based-Predictor (LS-EBP) emphasizes on image edges and make predictions accordingly. EBP is a gradient based nonlinear adaptive predictor. EBP switches between prediction-rules based on few threshold parameters automatically determined by a pre-analysis procedure, which makes a first pass. The LS-EBP also uses these parameters, but optimizes the prediction for each pre-analysis assigned edge location, thus applying least-square approach only at the edge points.;For encoding module: a novel Burrows Wheeler Transform (BWT) inspired method is suggested, which performs better than applying the BWT directly on the images. We also present a context-based adaptive error modeling and encoding scheme. When coupled with the above-mentioned prediction schemes, the result is the best-known compression performance in the genre of compression schemes with same time and space complexity

Prioritizing Content of Interest in Multimedia Data Compression

Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph

Intra-Key-Frame Coding and Side Information Generation Schemes in Distributed Video Coding

In this thesis investigation has been made to propose improved schemes for intra-key-frame coding and side information (SI) generation in a distributed video coding (DVC) framework. From the DVC developments in last few years it has been observed that schemes put more thrust on intra-frame coding and better quality side information (SI) generation. In fact both are interrelated as SI generation is dependent on decoded key frame quality. Hence superior quality key frames generated through intra-key frame coding will in turn are utilized to generate good quality SI frames. As a result, DVC needs less number of parity bits to reconstruct the WZ frames at the decoder. Keeping this in mind, we have proposed two schemes for intra-key frame coding namely, (a) Borrows Wheeler Transform based H.264/AVC (Intra) intra-frame coding (BWT-H.264/AVC(Intra)) (b) Dictionary based H.264/AVC (Intra) intra-frame coding using orthogonal matching pursuit (DBOMP-H.264/AVC (Intra)) BWT-H.264/AVC (Intra) scheme is a modified version of H.264/AVC (Intra) scheme where a regularized bit stream is generated prior to compression. This scheme results in higher compression efficiency as well as high quality decoded key frames. DBOMP-H.264/AVC (Intra) scheme is based on an adaptive dictionary and H.264/AVC (Intra) intra-frame coding. The traditional transform is replaced with a dictionary trained with K-singular value decomposition (K-SVD) algorithm. The dictionary elements are coded using orthogonal matching pursuit (OMP). Further, two side information generation schemes have been suggested namely, (a) Multilayer Perceptron based side information generation (MLP - SI) (b) Multivariable support vector regression based side information generation (MSVR-SI) MLP-SI scheme utilizes a multilayer perceptron (MLP) to estimate SI frames from the decoded key frames block-by-block. The network is trained offline using training patterns from different frames collected from standard video sequences. MSVR-SI scheme uses an optimized multi variable support vector regression (M-SVR) to generate SI frames from decoded key frames block-by-block. Like MLP, the training for M-SVR is made offline with known training patterns apriori. Both intra-key-frame coding and SI generation schemes are embedded in the Stanford based DVC architecture and studied individually to compare performances with their competitive schemes. Visual as well as quantitative evaluations have been made to show the efficacy of the schemes. To exploit the usefulness of intra-frame coding schemes in SI generation, four hybrid schemes have been formulated by combining the aforesaid suggested schemes as follows: (a) BWT-MLP scheme that uses BWT-H.264/AVC (Intra) intra-frame coding scheme and MLP-SI side information generation scheme. (b) BWT-MSVR scheme, where we utilize BWT-H.264/AVC (Intra) for intra-frame coding followed by MSVR-SI based side information generation. (c) DBOMP-MLP scheme is an outcome of putting DBOMP-H.264/AVC (Intra) intra-frame coding and MLP-SI side information generation schemes. (d) DBOMP-MSVR scheme deals with DBOMP-H.264/AVC (Intra) intra-frame coding and MSVR-SI side information generation together. The hybrid schemes are also incorporated into the Stanford based DVC architecture and simulation has been carried out on standard video sequences. The performance analysis with respect to overall rate distortion, number requests per SI frame, temporal evaluation, and decoding time requirement has been made to derive an overall conclusion

Read alignment using deep neural networks

2019 Spring.Includes bibliographical references.Read alignment is the process of mapping short DNA sequences into the reference genome. With the advent of consecutively evolving "next generation" sequencing technologies, the need for sequence alignment tools appeared. Many scientific communities and the companies marketing the sequencing technologies developed a whole spectrum of read aligners/mappers for different error profiles and read length characteristics. Among the most recent successfully marketed sequencing technologies are Oxford Nanopore and PacBio SMRT sequencing, which are considered top players because of their extremely long reads and low cost. However, the reads may contain error up to 20% that are not generally uniformly distributed. To deal with that level of error rate and read length, proximity preserving hashing techniques, such as Minhash and Minimizers, were utilized to quickly map a read to the target region of the reference sequence. Subsequently, a variant of global or local alignment dynamic programming is then used to give the final alignment. In this research work, we train a Deep Neural Network (DNN) to yield a hashing scheme for the highly erroneous long reads, which is deemed superior to Minhash for mapping the reads. We implemented that idea to build a read alignment tool: DNNAligner. We evaluated the performance of our aligner against the popular read aligners in the bioinformatics community currently — minimap2, bwa-mem and graphmap. Our results show that the performance of DNNAligner is comparable to other tools without any code optimization or integration of other advanced features. Moreover, DNN exhibits superior performance in comparison with Minhashon neighborhood classification

Intelligent detectors

Die vorliegende Arbeit stellt eine Basis zur Entwicklung von On-Board Software für astronomische Satelliten dar. Sie dient als Anleitung und Nachschlagewerk und zeigt anhand der Projekte Herschel/PACS und SPICA/SAFARI, wie aus den Grundlagen weltraumtaugliche Flugsoftware entsteht. Dazu gehören das Verstehen des wissenschaftlichen Zwecks, also was soll wie gemessen werden und wofür ist das gut, sowie die Kenntnis der physikalischen Eigenschaften des Detektors, das Beherrschen der mathematischen Operationen zur Verarbeitung der Daten und natürlich auch die Berücksichtigung der Umstände, unter welchen der Detektor zum Einsatz kommt.This thesis contains the knowledge and a good deal of experience that are necessary for the development of such astronomical on-board software for satellites. The key elements in the development are the understanding of the scientific purpose, knowledge of the physical properties of the detector, the comprehension of the mathematical operations involved in data processing and the consideration of the technical and observational circumstances

Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Less is More: Restricted Representations for Better Interpretability and Generalizability

Deep neural networks are prevalent in supervised learning for large amounts of tasks such as image classification, machine translation and even scientific discovery. Their success is often at the sacrifice of interpretability and generalizability. The increasing complexity of models and involvement of the pre-training process make the inexplicability more imminent. The outstanding performance when labeled data are abundant while prone to overfit when labeled data are limited demonstrates the difficulty of deep neural networks' generalizability to different datasets. This thesis aims to improve interpretability and generalizability by restricting representations. We choose to approach interpretability by focusing on attribution analysis to understand which features contribute to prediction on BERT, and to approach generalizability by focusing on effective methods in a low-data regime. We consider two strategies of restricting representations: (1) adding bottleneck, and (2) introducing compression. Given input x, suppose we want to learn y with the latent representation z (i.e. x→z→y), adding bottleneck means adding function R such that L(R(z)) < L(z) and introducing compression means adding function R so that L(R(y)) < L(y) where L refers to the number of bits. In other words, the restriction is added either in the middle of the pipeline or at the end of it. We first introduce how adding information bottleneck can help attribution analysis and apply it to investigate BERT's behavior on text classification in Chapter 3. We then extend this attribution method to analyze passage reranking in Chapter 4, where we conduct a detailed analysis to understand cross-layer and cross-passage behavior. Adding bottleneck can not only provide insight to understand deep neural networks but can also be used to increase generalizability. In Chapter 5, we demonstrate the equivalence between adding bottleneck and doing neural compression. We then leverage this finding with a framework called Non-Parametric learning by Compression with Latent Variables (NPC-LV), and show how optimizing neural compressors can be used in the non-parametric image classification with few labeled data. To further investigate how compression alone helps non-parametric learning without latent variables (NPC), we carry out experiments with a universal compressor gzip on text classification in Chapter 6. In Chapter 7, we elucidate methods of adopting the perspective of doing compression but without the actual process of compression using T5. Using experimental results in passage reranking, we show that our method is highly effective in a low-data regime when only one thousand query-passage pairs are available. In addition to the weakly supervised scenario, we also extend our method to large language models like GPT under almost no supervision --- in one-shot and zero-shot settings. The experiments show that without extra parameters or in-context learning, GPT can be used for semantic similarity, text classification, and text ranking and outperform strong baselines, which is presented in Chapter 8. The thesis proposes to tackle two big challenges in machine learning --- "interpretability" and "generalizability" through restricting representation. We provide both theoretical derivation and empirical results to show the effectiveness of using information-theoretic approaches. We not only design new algorithms but also provide numerous insights on why and how "compression" is so important in understanding deep neural networks and improving generalizability