The B-coder: an improved binary arithmetic coder and probability estimator

In this paper we present the B-coder, an efficient binary arithmetic coder that performs extremely well on a wide range of data. The B-coder should be classed as an `approximate’ arithmetic coder, because of its use of an approximation to multiplication. We show that the approximation used in the B-coder has an efficiency cost of 0.003 compared to Shannon entropy. At the heart of the B-coder is an efficient state machine that adapts rapidly to the data to be coded. The adaptation is achieved by allowing a fixed table of transitions and probabilities to change within a given tolerance. The combination of the two techniques gives a coder that out-performs the current state-of-the-art binary arithmetic coders

The B-coder: an improved binary arithmetic coder and probability estimator

In this paper we present the B-coder, an efficient binary arithmetic coder that performs extremely well on a wide range of data. The B-coder should be classed as an `approximate’ arithmetic coder, because of its use of an approximation to multiplication. We show that the approximation used in the B-coder has an efficiency cost of 0.003 compared to Shannon entropy. At the heart of the B-coder is an efficient state machine that adapts rapidly to the data to be coded. The adaptation is achieved by allowing a fixed table of transitions and probabilities to change within a given tolerance. The combination of the two techniques gives a coder that out-performs the current state-of-the-art binary arithmetic coders

An approach to summarize video data in compressed domain

Thesis (Master)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2007Includes bibliographical references (leaves: 54-56)Text in English; Abstract: Turkish and Englishx, 59 leavesThe requirements to represent digital video and images efficiently and feasibly have collected great efforts on research, development and standardization over past 20 years. These efforts targeted a vast area of applications such as video on demand, digital TV/HDTV broadcasting, multimedia video databases, surveillance applications etc. Moreover, the applications demand more efficient collections of algorithms to enable lower bit rate levels, with acceptable quality depending on application requirements. In our time, most of the video content either stored, transmitted is in compressed form. The increase in the amount of video data that is being shared attracted interest of researchers on the interrelated problems of video summarization, indexing and abstraction. In this study, the scene cut detection in emerging ISO/ITU H264/AVC coded bit stream is realized by extracting spatio-temporal prediction information directly in the compressed domain. The syntax and semantics, parsing and decoding processes of ISO/ITU H264/AVC bit-stream is analyzed to detect scene information. Various video test data is constructed using Joint Video Team.s test model JM encoder, and implementations are made on JM decoder. The output of the study is the scene information to address video summarization, skimming, indexing applications that use the new generation ISO/ITU H264/AVC video

Dynamic Region and Block-Based Motion Estimation for Video Compression

The aim of this project is to find a motion estimation method that works in combination with block matching in order to reduce the visible artifacts. The proposed solution tries to extract the real motions taking place in a sequence. The developed algorithm is a region based motion estimator. We associate to regions of general shape motion parameters which describe an ane transformation on the plane. The traditional block matching is then used for smaller transformations. The developed algorithm is iterative. It alternatively considers the motion and the region as being constant and renes the other one according to this hypothesis. The results will show that it converges and leads to good results if big objects are moving in a sequence

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

Mu-2 ranging

The Mu-II Dual-Channel Sequential Ranging System designed as a model for future Deep Space Network ranging equipment is described. A list of design objectives is followed by a theoretical explanation of the digital demodulation techniques first employed in this machine. Hardware and software implementation are discussed, together with the details relating to the construction of the device. Two appendixes are included relating to the programming and operation of this equipment to yield the maximum scientific data