Lossless Compression Using LFSRs

Different medical devices for imaging used by centers and clinics produce an increasing number of sequential medical images. ‎Different imaging techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Fluoroscopy ‎produce a set of series for the same patient. Within these images, most of the image parts are fixed against noticeable changes ‎in the remaining part. This consumes non-ignorable storage space. This paper proposes a near-lossless compression ‎technique that considers the fixed image parts to focus on changing parts for a higher compression ratio. In some applications, lossless compression techniques are highly preferable against preferring lossy techniques in some applications. In other applications, near-lossless compression techniques are preferable to lossless and lossy compression techniques, where lossy ones may ‎lose significant details, and the lossless ones produce less compression ratios than near-lossless ones. Previous works dealt with Fluoroscopy images as individual images or using ‎video compression techniques. This work tends to handle the whole series of ‎images as an integrated object. This paper considers subtracting successive ‎images to detect ROI areas producing zero overall values over similar ‎areas and non-zero ones within ROI ones. The double coding technique and near-lossless concept of compression increase the compression ratio. ‎Conducted experiments showed encouraging results benchmarking the other published ‎works in medical image compression.‎

Research at the Space Dynamics Laboratory(SDL) / Utah State University in image compression has resulted in the development of a hybrid compression algorithm for space related images. The basic algorithm is a hybrid of lossy and lossless compression techniques that have been implemented in hardware [I]. The lossy encoder uses Mean Removed Vector Quantization(MRVQ). The Lossless compressor is an entropy encoder used to encode the errors introduced by the lossy MRVQ. A constant bit rate is maintained by thresholding the MRVQ errors based on the number of previously encoded bits. This paper compares Huffman and Rice Compression techniques to be used in the lossless stage of the hybrid compressor. These method have been selected because hardware to implement them already exist. A single chip implementation of the Rice algorithm is available that processes at the rates required[2].

Airborne light detection and ranging (LIDAR) system uses in many field such as military, agriculture, geology and transportation. LIDAR data acquisition consumes several gigabytes from the storage capacity of the system. So, it's necessary to reduce the on board storage capacity as well as its transmission rate. Compression of LIDAR data means reducing the amount of data required to represent the digital data acquired by LIDAR system. This is done by removing data redundancies to achieve compression. In this paper we explain LIDAR system theory and component, compare between lossy and lossless compression techniques then we discuss LIDAR data compression techniques in order to choose a suitable one for LIDAR system that gives high compression ratio with suitable processing time. In most LIDAR data compression techniques classification of LIDAR data is an important issue as a preprocessing step to simplify the data. Using more than one compression algorithms increase the compression ratio but increase the processing time as well.

State-of-the-art graphic processing units (GPUs) provide very high memory bandwidth, but the performance of many general-purpose GPU (GPGPU) workloads is still bounded by memory bandwidth. Although compression techniques have been adopted by commercial GPUs, they are only used for compressing texture and color data, not data for GPGPU workloads. Furthermore, the microarchitectural details of GPU compression are proprietary and its performance benefits have not been previously published. In this paper, we first investigate required microarchitectural changes to support lossless compression techniques for data transferred between the GPU and its off-chip memory to provide higher effective bandwidth. Second, by exploiting some characteristics of floating-point numbers in many GPGPU workloads, we propose to apply lossless compression to floating-point numbers after truncating their least-significant bits (i.e., lossy compression). This can reduce the bandwidth usage even further with very little impact on overall computational accuracy. Finally, we demonstrate that a GPU with our lossless and lossy compression techniques can improve the performance of memory-bound GPGPU workloads by 26% and 41% on average.

In LHC Run 3, the upgraded ALICE detector will record 50 kHz Pb-Pb collisions using continuous readout. The resulting stream of raw data to be inspected increases to ~ 1 TB/s a hundredfold increase over Run 2 must be processed with a set of lossy and lossless compression and data reduction techniques to decrease the data rate to storage to 90 GB/s without affecting the physics. This contribution focuses on lossless entropy coding for ALICE Run 3 data which is the final component in the compression stage. We analyze data from the ALICE TPC and point out the challenges imposed by the non-standard data with a patchy distribution and symbol sizes of up to 25 Bit. We then explain why rANS, a variant of Asymmetric Numeral System coders is suitable for compressing this data effectively. Finally we present first compression performance numbers and bandwidth measurements obtained from a prototype implementation and give an outlook for future developments.

Technique in carrying out data compression is an important point in technological developments. With compression in data in the form of text can include many uses, including for data transfer, copying and for backing up data. From its uses, this aspect is important for data security. There are many compression techniques on the data, including using huffman algorithms and unary code. One of its applications will be implemented on a text data that is widely used by digital actors in storing important data. The data must not be known by unauthorized parties in accessing the data. Therefore, huffman algorithms and unary code can solve this problem. By compressing the selected data also encrypts it as an extra security. The Huffman algorithm is a lossless compression algorithm or a technique that does not change the original data, by converting the unit of data content into bits. So this algorithm is widely used in the compression process. The Unary Codes algorithm is also a lossless compression technique that is generally used by combining several modification techniques. In this unary codes algorithm, each symbol in the string will be searched for its frequency. Then sorted from the last order (descending). The use of these two text data compression techniques results in a file size that is smaller than the original but can be returned to the original data

This paper presents performance comparison of lossless compression technique based on wavelets with progressive transmission capacity. Compressed images are used in various applications, where a large database has to be maintained. To store and retrieve such a large database lossless compression is essential. The performance achieved by this scheme is better than the set partitioning in hierarchical trees (SPIHT). The work also consists of comparative performances of various lossless and lossy compression techniques with progressive transmission capabilities. Compression ratio of 90 to 95% is achieved by this scheme.

To address the performance saturation in communication data path at migrating BigData, the lossless data compression technique is a solution to enhance the bandwidth of the path. However, the recent data compression mechanism needs to treat data streams such as sensor data with very low latency to avoid overhead in the path. This paper focuses on a new loss less data compression mechanism called LCA-DLT that implements a hardware-based fast stream lossless data compression using dictionary-based symbol lookup mechanism. When applying it to a very fast path, the hardware latency increases largely and the clock speed degrades because the dictionary lookup operation becomes bottleneck of the longest delay path in the hardware. This paper proposes a performance improvement technique applying multithreading technique in the dictionary lookup operation. The technique enables a single module of the LCA-DLT to accept multiple data streams by dividing the compression timing in babble stage of the compression/decompression pipeline. According to performance evaluation by a hardware implementation with two threads, although the data compression bandwidth logically becomes half of the original single thread LCA-DLT, the time-sharing multithreading technique reduces required hardware resources and improves the clock frequency.

This paper provides different kinds of techniques for lossless data compression and comparison between them. By eliminating redundant bits, data compression decreases the file size. In order to reduce the capacity needed for that data, it decreases the redundant bits in data representation and thus uses the bandwidth effectively to reduce the communication cost. Compression of data saves file volume, network bandwidth and speeds up the transfer speed as well. Lossless and Lossy are the two techniques for data compression. Lossless compression maintains the data properly.

Compression is essential for efficient storage and transmission of three-dimensional (3D) digital holograms. The inherent speckle content in holographic data causes lossless compression techniques, such as Huffman and Burrows-Wheeler (BW), to perform poorly. Therefore, the combination of lossy quantisation followed by lossless compression is essential for effective compression of digital holograms. Our complex-valued digital holograms of 3D real-world objects were captured using phase-shift interferometry (PSI). Quantisation reduces the number of different real and imaginary values required to describe each hologram. Traditional data compression techniques can then be applied to the hologram to actually reduce its size. Since our data has a nonuniform distribution, the uniform quantisation technique does not perform optimally. We require nonuniform quantisation, since in a histogram representation our data is denser around the origin (low amplitudes), thus requiring more cluster centres, and sparser away from the origin (high amplitudes). By nonuniformly positioning the cluster centres to match the fact that there is a higher probability that the pixel will have a low amplitude value, the cluster centres can be used more efficiently. Nonuniform quantisation results in cluster centres that are adapted to the exact statistics of the input data. We analyse a number of iterative (k-means clustering, Kohonen competitive neural network, SOM, and annealed Hopfield neural network), and non-iterative (companding, histogram, and optimal) nonuniform quantisation techniques. We discuss the strengths and weaknesses of each technique and highlight important factors that must be considered when choosing between iterative and non-iterative nonuniform quantisation. We measure the degradation due to lossy quantisation in the reconstruction domain, using the normalised rms (NRMS) metric.

This paper presents a promising time-domain based lossless compression technique called Log2 Sub-band encoding, which is designed for using on wireless biomedical devices. Data compression can help to save power from the wireless transceiver during data transmission, and from the storage medium during reading and writing, ultimately leading to a longer battery life of the device. The performance of Log2 Sub-band is measured in terms of its compression ratio (CR) on EEG data and its power consumption. Our simulation results indicate a CR that is comparable and even superior to the well-known Huffman coding, whilst consuming minimal hardware resource. The simulations primarily use electroencephalogram (EEG) data, and the power consumption during compressing process is given to evaluate the system's improvement on its power performance. The possible influence of different biomedical data on technique's performance will also be considered and the signal classification potential of Log2 Sub-band will be noted.

Continually increasing amounts of data being generated on spacecraft are driving the need for faster and more power-efficient processing. However, it remains a challenge to determine whether the best option for a given mission scenario is to (1) transmit raw data to ground stations for terrestrial processing, (2) compress data on-board and transmit compressed data to ground stations for terrestrial decompression and processing, or (3) process data on-board and transmit processed data to ground stations. In this study, results are generated and analyzed for timing and energy usage for various mission scenarios using commercial GPUs, military-grade radio transmitters, a neural network application, and both lossy and lossless image compression techniques, all operating on satellite imagery. Results are used to demonstrate how to identify the most efficient data processing options to enable better design and planning for future space missions.

The past few years have seen an increasing interest in using reversible integer wavelets in image compression. Reversible integer wavelet image coders facilitate decompression from low bit rates all the way up to lossless reconstruction. However, in the past, specific implementations of such techniques, like S+P, could not match the lossless compression performance of state-of-the-art predictive coding techniques like CALIC. We demonstrate for the first time that reversible integer wavelets together with proper context modeling can match the lossless compression performance of CALIC. This can be done without increase in the essential complexity over S+P. Our findings present a strong argument for using subband coding as a unified, elegant approach for both lossy and lossless image compression. Specifically, in this paper we outline how to obtain significantly higher coding efficiency over S+P by utilizing better filters and better context modeling and entropy coding of wavelet coefficients.

Space research organizations, hospitals and military air surveillance activities, among others, produce a huge amount of data in the form of images hence a large storage space is required to record this information. In hospitals, data produced during medical examination is in the form of a sequence of images and are very much correlated; because these images have great importance, some kind of lossless image compression technique is needed. Moreover, these images are often required to be transmitted over the network. Since the availability of storage and bandwidth is limited, a compression technique is required to reduce the number of bits to store these images and take less time to transmit them over the network. For this purpose, there are many state-of the-art lossless image compression algorithms like CALIC, LOCO-I, JPEG-LS, JPEG20000; Nevertheless, these compression algorithms take only a single file to compress and cannot exploit the correlation among the sequence frames of MRI or CE images. To exploit the correlation, a new algorithm is proposed in this paper. The primary goals of the proposed compression method are to minimize the memory resource during storage of compressed data as well as minimize the bandwidth requirement during transmission of compressed data. For achieving these goals, the proposed compression method combines the single image compression technique called super spatial structure prediction with inter-frame coding to acquire grater compression ratio. An efficient compression method requires elimination of redundancy of data during compression; therefore, for elimination of redundancy of data, initially, the super spatial structure prediction algorithm is applied with the fast block matching approach and later Huffman coding is applied for reducing the number of bits required for transmitting and storing single pixel value. Also, to speed up the block-matching process during motion estimation, the proposed method compares those blocks that have identical sum and leave the others; therefore, the time taken by the block-matching process is reduced by minimizing the unnecessary overhead during the blockmatching process. Thus, in the proposed fast lossless compression method for medical image sequences, the twostage redundant data elimination process ultimately reduces the memory resource required for storing and transmission. The method is tested on the sequences of MRI and CE images and produces an improved compression rate.

In this paper, a 4-bit Linear Feedback Shift Register (LFSR) is implemented based on a well-designed architecture combining using Pass-Transistor (PT) and Clock Complementary Metal Oxide Semiconductor (C2MOS) logic techniques. The D-flip flop registers and the XOR gates are the main parts of the propose LFSR. Number of transistors along with the speed of LFSR were positively enhanced since the exploited logic design techniques tends to blend the flavor of NMOS and PMOS devices. The PSpice and Field Programmable Gate Array (FPGA) based on Hardware Description Language (HDL) are the two different LFSR implementation environments. It has been observed that LFSR performance was effectively improved in terms of size and speed. Therefore, paper’s main aim refers to decreasing in number of transistors as well as speeding up LFSR circuit. A minimum clock time of 5ns was recorded under clearly correct LFSR output patterns. The LFSR circuit Based CMOS techniques can reduce the exploited transistors up to 43% of the conventional C2MOS logic. Besides, the number of transistors being reduced reflects the achieved circuit’s size reduction. It was concluded that paying attention to the design of the main LFSR circuit parts contributes mainly in the enhancement of the whole circuit performance.