Segmentation-based lossless compression of burn wound images

Color images may be encoded by using a gray-scale image compression technique on each of the three color planes. Such an approach, however, does not take advantage of the correlation existing between the color planes. In this paper, a new segmentation-based lossless compression method is proposed for color images. The method exploits the correlation existing among the three color planes by treating each pixel as a vector of three components, performing region growing and difference operations using the vectors, and applying a color coordinate transformation. The method performed better than the Joint Photographic Experts Group (JPEG) standard by an average of 3.40 bits/pixel with a database including four natural color images of scenery, four images of burn wounds, and four fractal images, and it outperformed the Joint Bi-Level Image experts Group (JBIG) standard by an average of 3.01 bits/pixel. When applied to a database of 20 burn wound images, the 24 bits/pixel images were efficiently compressed to 4.79 bits/pixel, then requiring 4.16 bits/pixel less than JPEG and 5.41 bits/pixel less than JBIG

Compression Methods for Structured Floating-Point Data and their Application in Climate Research

The use of new technologies, such as GPU boosters, have led to a dramatic increase in the computing power of High-Performance Computing (HPC) centres. This development, coupled with new climate models that can better utilise this computing power thanks to software development and internal design, led to the bottleneck moving from solving the differential equations describing Earth’s atmospheric interactions to actually storing the variables. The current approach to solving the storage problem is inadequate: either the number of variables to be stored is limited or the temporal resolution of the output is reduced. If it is subsequently determined that another vari- able is required which has not been saved, the simulation must run again. This thesis deals with the development of novel compression algorithms for structured floating-point data such as climate data so that they can be stored in full resolution. Compression is performed by decorrelation and subsequent coding of the data. The decorrelation step eliminates redundant information in the data. During coding, the actual compression takes place and the data is written to disk. A lossy compression algorithm additionally has an approx- imation step to unify the data for better coding. The approximation step reduces the complexity of the data for the subsequent coding, e.g. by using quantification. This work makes a new scientific contribution to each of the three steps described above. This thesis presents a novel lossy compression method for time-series data using an Auto Regressive Integrated Moving Average (ARIMA) model to decorrelate the data. In addition, the concept of information spaces and contexts is presented to use information across dimensions for decorrela- tion. Furthermore, a new coding scheme is described which reduces the weaknesses of the eXclusive-OR (XOR) difference calculation and achieves a better compression factor than current lossless compression methods for floating-point numbers. Finally, a modular framework is introduced that allows the creation of user-defined compression algorithms. The experiments presented in this thesis show that it is possible to in- crease the information content of lossily compressed time-series data by applying an adaptive compression technique which preserves selected data with higher precision. An analysis for lossless compression of these time- series has shown no success. However, the lossy ARIMA compression model proposed here is able to capture all relevant information. The reconstructed data can reproduce the time-series to such an extent that statistically rele- vant information for the description of climate dynamics is preserved. Experiments indicate that there is a significant dependence of the com- pression factor on the selected traversal sequence and the underlying data model. The influence of these structural dependencies on prediction-based compression methods is investigated in this thesis. For this purpose, the concept of Information Spaces (IS) is introduced. IS contributes to improv- ing the predictions of the individual predictors by nearly 10% on average. Perhaps more importantly, the standard deviation of compression results is on average 20% lower. Using IS provides better predictions and consistent compression results. Furthermore, it is shown that shifting the prediction and true value leads to a better compression factor with minimal additional computational costs. This allows the use of more resource-efficient prediction algorithms to achieve the same or better compression factor or higher throughput during compression or decompression. The coding scheme proposed here achieves a better compression factor than current state-of-the-art methods. Finally, this paper presents a modular framework for the development of compression algorithms. The framework supports the creation of user- defined predictors and offers functionalities such as the execution of bench- marks, the random subdivision of n-dimensional data, the quality evalua- tion of predictors, the creation of ensemble predictors and the execution of validity tests for sequential and parallel compression algorithms. This research was initiated because of the needs of climate science, but the application of its contributions is not limited to it. The results of this the- sis are of major benefit to develop and improve any compression algorithm for structured floating-point data

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

Remote access computed tomography colonography

This thesis presents a novel framework for remote access Computed Tomography Colonography (CTC). The proposed framework consists of several integrated components: medical image data delivery, 2D image processing, 3D visualisation, and feedback provision. Medical image data sets are notoriously large and preserving the integrity of the patient data is essential. This makes real-time delivery and visualisation a key challenge. The main contribution of this work is the development of an efficient, lossless compression scheme to minimise the size of the data to be transmitted, thereby alleviating transmission time delays. The scheme utilises prior knowledge of anatomical information to divide the data into specific regions. An optimised compression method for each anatomical region is then applied. An evaluation of this compression technique shows that the proposed ‘divide and conquer’ approach significantly improves upon the level of compression achieved using more traditional global compression schemes. Another contribution of this work resides in the development of an improved volume rendering technique that provides real-time 3D visualisations of regions within CTC data sets. Unlike previous hardware acceleration methods which rely on dedicated devices, this approach employs a series of software acceleration techniques based on the characteristic properties of CTC data. A quantitative and qualitative evaluation indicates that the proposed method achieves real-time performance on a low-cost PC platform without sacrificing any image quality. Fast data delivery and real-time volume rendering represent the key features that are required for remote access CTC. These features are ultimately combined with other relevant CTC functionality to create a comprehensive, high-performance CTC framework, which makes remote access CTC feasible, even in the case of standard Web clients with low-speed data connections

Analysis of Image Compression Methods Based On Transform and Fractal Coding

Image compression is process to remove the redundant information from the image so that only essential information can be stored to reduce the storage size, transmission bandwidth and transmission time. The essential information is extracted by various transforms techniques such that it can be reconstructed without losing quality and information of the image. In this thesis work comparative analysis of image compression is done by four transform method, which are Discrete Cosine Transform (DCT), Discrete Wavelet Transform( DWT) & Hybrid (DCT+DWT) Transform and fractal coding. MATLAB programs were written for each of the above method and concluded based on the results obtained that hybrid DWT-DCT algorithm performs much better than the standalone JPEG-based DCT, DWT algorithms in terms of peak signal to noise ratio (PSNR), as well as visual perception at higher compression ratio. The popular JPEG standard is widely used in digital cameras and web ¨Cbased image delivery. The wavelet transform, which is part of the new JPEG 2000 standard, claims to minimize some of the visually distracting artifacts that can appear in JPEG images. For one thing, it uses much larger blocks- selectable, but typically1024 x 1024 pixels ¨C for compression, rather than the 8 X 8 pixel blocks used in the original JPEG method, which often produced visible boundaries. Fractal compression has also shown promise and claims to be able to enlarge images by inserting ¨Drealistic¡¬ detail beyond the resolution limit of the original. Each method is discussed in the thesis

Image Compression Techniques: A Survey in Lossless and Lossy algorithms

The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression

3D Medical Image Lossless Compressor Using Deep Learning Approaches

The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in efficient handling. Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies effectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an effective compression method. Since medical information is critical and imposes an influential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable efforts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information efficiently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the differences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the effectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input configurations and sampling schemes for a many-to-one sequence prediction model, specifically for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by different hospitals, representing different body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are significantly more informative than others, specifically in medical domains where samples are available on a scale of billions. The effectiveness of models trained on the presented importance sampling scheme was evaluated compared to alternative strategies such as uniform, Gaussian, and sliced-based sampling

Investigating Polynomial Fitting Schemes for Image Compression

Image compression is a means to perform transmission or storage of visual data in the most economical way. Though many algorithms have been reported, research is still needed to cope with the continuous demand for more efficient transmission or storage. This research work explores and implements polynomial fitting techniques as means to perform block-based lossy image compression. In an attempt to investigate nonpolynomial models, a region-based scheme is implemented to fit the whole image using bell-shaped functions. The idea is simply to view an image as a 3D geographical map consisting of hills and valleys. However, the scheme suffers from high computational demands and inferiority to many available image compression schemes. Hence, only polynomial models get further considerations. A first order polynomial (plane) model is designed to work in a multiplication- and division-free (MDF) environment. The intensity values of each image block are fitted to a plane and the parameters are then quantized and coded. Blocking artefacts, a common drawback of block-based image compression techniques, are reduced using an MDF line-fitting scheme at blocks’ boundaries. It is shown that a compression ratio of 62:1 at 28.8dB is attainable for the standard image PEPPER, outperforming JPEG, both objectively and subjectively for this part of the rate-distortion characteristics. Inter-block prediction can substantially improve the compression performance of the plane model to reach a compression ratio of 112:1 at 27.9dB. This improvement, however, slightly increases computational complexity and reduces pipelining capability. Although JPEG2000 is not a block-based scheme, it is encouraging that the proposed prediction scheme performs better in comparison to JPEG 2000, computationally and qualitatively. However, more experiments are needed to have a more concrete comparison. To reduce blocking artefacts, a new postprocessing scheme, based on Weber’s law, is employed. It is reported that images postprocessed using this scheme are subjectively more pleasing with a marginal increase in PSNR (<0.3 dB). The Weber’s law is modified to perform edge detection and quality assessment tasks. These results motivate the exploration of higher order polynomials, using three parameters to maintain comparable compression performance. To investigate the impact of higher order polynomials, through an approximate asymptotic behaviour, a novel linear mapping scheme is designed. Though computationally demanding, the performances of higher order polynomial approximation schemes are comparable to that of the plane model. This clearly demonstrates the powerful approximation capability of the plane model. As such, the proposed linear mapping scheme constitutes a new approach in image modeling, and hence worth future consideration

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