Virtually Lossless Compression of Astrophysical Images

We describe an image compression strategy potentially capable of preserving the scientific quality of astrophysical data, simultaneously allowing a consistent bandwidth reduction to be achieved. Unlike strictly lossless techniques, by which moderate compression ratios are attainable, and conventional lossy techniques, in which the mean square error of the decoded data is globally controlled by users, near-lossless methods are capable of locally constraining the maximum absolute error, based on user's requirements. An advanced lossless/near-lossless differential pulse code modulation (DPCM) scheme, recently introduced by the authors and relying on a causal spatial prediction, is adjusted to the specific characteristics of astrophysical image data (high radiometric resolution, generally low noise, etc.). The background noise is preliminarily estimated to drive the quantization stage for high quality, which is the primary concern in most of astrophysical applications. Extensive experimental results of lossless, near-lossless, and lossy compression of astrophysical images acquired by the Hubble space telescope show the advantages of the proposed method compared to standard techniques like JPEG-LS and JPEG2000. Eventually, the rationale of virtually lossless compression, that is, a noise-adjusted lossles/near-lossless compression, is highlighted and found to be in accordance with concepts well established for the astronomers' community

ADAPTIVE CHANNEL AND SOURCE CODING USING APPROXIMATE INFERENCE

Channel coding and source coding are two important problems in communications. Although both channel coding and source coding (especially, the distributed source coding (DSC)) can achieve their ultimate performance by knowing the perfect knowledge of channel noise and source correlation, respectively, such information may not be always available at the decoder side. The reasons might be because of the time−varying characteristic of some communication systems and sources themselves, respectively. In this dissertation, I mainly focus on the study of online channel noise estimation and correlation estimation by using both stochastic and deterministic approximation inferences on factor graphs.In channel coding, belief propagation (BP) is a powerful algorithm to decode low−density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. To solve the problem, two common workarounds in approximate inference are stochastic methods (e.g. particle filtering (PF)) and deterministic methods (e.g. expectation approximation (EP)). Generally, deterministic methods are much faster than stochastic methods. In contrast, stochastic methods are more flexible and suitable for any distribution. In this dissertation, I proposed two adaptive LDPC decoding schemes, which are able to perform online estimation of time−varying channel state information (especially signal to noise ratio (SNR)) at the bit−level by incorporating PF and EP algorithms. Through experimental results, I compare the performance between the proposed PF based and EP based approaches, which shows that the EP based approach obtains the comparable estimation accuracy with less computational complexity than the PF based method for both stationary and time−varying SNR, and enhances the BP decoding performance simultaneously. Moreover, the EP estimator shows a very fast convergence speed, and the additional computational overhead of the proposed decoder is less than 10% of the standard BP decoder.Moreover, since the close relationship between source coding and channel coding, the proposed ideas are extended to source correlation estimation. First, I study the correlation estimation problem in lossless DSC setup, where I consider both asymmetric and non−asymmetric SW coding of two binary correlated sources. The aforementioned PF and EP based approaches are extended to handle the correlation between two binary sources, where the relationship is modeled as a virtual binary symmetric channel (BSC) with a time−varying crossover probability. Besides, to handle the correlation estimation problem of Wyner−Ziv (WZ) coding, a lossy DSC setup, I design a joint bit−plane model, by which the PF based approach can be applied to tracking the correlation between non−binary sources. Through experimental results, the proposed correlation estimation approaches significantly improve the compression performance of DSC.Finally, due to the property of ultra−low encoding complexity, DSC is a promising technique for many tasks, in which the encoder has only limited computing and communication power, e.g. the space imaging systems. In this dissertation, I consider a real−world application of the proposed correlation estimation scheme on the onboard low−complexity compression of solar stereo images, since such solutions are essential to reduce onboard storage, processing, and communication resources. In this dissertation, I propose an adaptive distributed compression solution using PF that tracks the correlation, as well as performs disparity estimation, at the decoder side. The proposed algorithm istested on the stereo solar images captured by the twin satellites systemof NASA’s STEREO project. The experimental results show the significant PSNR improvement over traditional separate bit−plane decoding without dynamic correlation and disparity estimation

On the information theory of clustering, registration, and blockchains

Progress in data science depends on the collection and storage of large volumes of reliable data, efficient and consistent inference based on this data, and trusting such computations made by untrusted peers. Information theory provides the means to analyze statistical inference algorithms, inspires the design of statistically consistent learning algorithms, and informs the design of large-scale systems for information storage and sharing. In this thesis, we focus on the problems of reliability, universality, integrity, trust, and provenance in data storage, distributed computing, and information processing algorithms and develop technical solutions and mathematical insights using information-theoretic tools. In unsupervised information processing we consider the problems of data clustering and image registration. In particular, we evaluate the performance of the max mutual information method for image registration by studying its error exponent and prove its universal asymptotic optimality. We further extend this to design the max multiinformation method for universal multi-image registration and prove its universal asymptotic optimality. We then evaluate the non-asymptotic performance of image registration to understand the effects of the properties of the image transformations and the channel noise on the algorithms. In data clustering we study the problem of independence clustering of sources using multivariate information functionals. In particular, we define consistent image clustering algorithms using the cluster information, and define a new multivariate information functional called illum information that inspires other independence clustering methods. We also consider the problem of clustering objects based on labels provided by temporary and long-term workers in a crowdsourcing platform. Here we define budget-optimal universal clustering algorithms using distributional identicality and temporal dependence in the responses of workers. For the problem of reliable data storage, we consider the use of blockchain systems, and design secure distributed storage codes to reduce the cost of cold storage of blockchain ledgers. Additionally, we use dynamic zone allocation strategies to enhance the integrity and confidentiality of these systems, and frame optimization problems for designing codes applicable for cloud storage and data insurance. Finally, for the problem of establishing trust in computations over untrusting peer-to-peer networks, we develop a large-scale blockchain system by defining the validation protocols and compression scheme to facilitate an efficient audit of computations that can be shared in a trusted manner across peers over the immutable blockchain ledger. We evaluate the system over some simple synthetic computational experiments and highlights its capacity in identifying anomalous computations and enhancing computational integrity

Parallel implementation of fractal image compression

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.Fractal image compression exploits the piecewise self-similarity present in real images as a form of information redundancy that can be eliminated to achieve compression. This theory based on Partitioned Iterated Function Systems is presented. As an alternative to the established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal techniques promise faster decoding and potentially higher fidelity, but the computationally intensive compression process has prevented commercial acceptance. This thesis presents an algorithm mapping the problem onto a parallel processor architecture, with the goal of reducing the encoding time. The experimental work involved implementation of this approach on the Texas Instruments TMS320C80 parallel processor system. Results indicate that the fractal compression process is unusually well suited to parallelism with speed gains approximately linearly related to the number of processors used. Parallel processing issues such as coherency, management and interfacing are discussed. The code designed incorporates pipelining and parallelism on all conceptual and practical levels ensuring that all resources are fully utilised, achieving close to optimal efficiency. The computational intensity was reduced by several means, including conventional classification of image sub-blocks by content with comparisons across class boundaries prohibited. A faster approach adopted was to perform estimate comparisons between blocks based on pixel value variance, identifying candidates for more time-consuming, accurate RMS inter-block comparisons. These techniques, combined with the parallelism, allow compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB PSNR. This is up to an order of magnitude faster than reported for conventional sequential processor implementations. Fractal based compression of colour images and video sequences is also considered. The work confirms the potential of fractal compression techniques, and demonstrates that a parallel implementation is appropriate for addressing the compression time problem. The processor system used in these investigations is faster than currently available PC platforms, but the relevance lies in the anticipation that future generations of affordable processors will exceed its performance. The advantages of fractal image compression may then be accessible to the average computer user, leading to commercial acceptance

Structured video coding

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1991.Includes bibliographical references (leaves 67-71).by Patrick Campbell McLean.M.S

Parallel implementation of fractal image compression

Thesis (M.Sc.Eng.)-University of Natal, Durban, 2000.Fractal image compression exploits the piecewise self-similarity present in real images as a form of information redundancy that can be eliminated to achieve compression. This theory based on Partitioned Iterated Function Systems is presented. As an alternative to the established JPEG, it provides a similar compression-ratio to fidelity trade-off. Fractal techniques promise faster decoding and potentially higher fidelity, but the computationally intensive compression process has prevented commercial acceptance. This thesis presents an algorithm mapping the problem onto a parallel processor architecture, with the goal of reducing the encoding time. The experimental work involved implementation of this approach on the Texas Instruments TMS320C80 parallel processor system. Results indicate that the fractal compression process is unusually well suited to parallelism with speed gains approximately linearly related to the number of processors used. Parallel processing issues such as coherency, management and interfacing are discussed. The code designed incorporates pipelining and parallelism on all conceptual and practical levels ensuring that all resources are fully utilised, achieving close to optimal efficiency. The computational intensity was reduced by several means, including conventional classification of image sub-blocks by content with comparisons across class boundaries prohibited. A faster approach adopted was to perform estimate comparisons between blocks based on pixel value variance, identifying candidates for more time-consuming, accurate RMS inter-block comparisons. These techniques, combined with the parallelism, allow compression of 512x512 pixel x 8 bit images in under 20 seconds, while maintaining a 30dB PSNR. This is up to an order of magnitude faster than reported for conventional sequential processor implementations. Fractal based compression of colour images and video sequences is also considered. The work confirms the potential of fractal compression techniques, and demonstrates that a parallel implementation is appropriate for addressing the compression time problem. The processor system used in these investigations is faster than currently available PC platforms, but the relevance lies in the anticipation that future generations of affordable processors will exceed its performance. The advantages of fractal image compression may then be accessible to the average computer user, leading to commercial acceptance