Effective bitstream compression approaches for high speed digital systems

The electrocardiogram (ECG) is a salient signal that is commonly utilized to diagnose heart patients. The recording of ECG signals generates a large amount of data when continuous monitoring of the heart is necessary. Hence, there is a strong motivation to develop a suitable compression technique to minimize bandwidth and memory requirements. In this context, this work proposes a compression technique using tunable-Q wavelet transform (TQWT) and an optimized dead-zone quantizer (ODZQ). The TQWT is used for the decomposition of ECG signal and DZQ for thresholding and quantization. The swarm-based method, particle swarm optimization (PSO) is used to obtain the optimized threshold values. The compressed signal is obtained by thresholding, quantization, and encoding of quantized coefficients. Encoding is performed by utilizing run-length encoding (RLE), which helps to achieve further compression. The proposed method is assessed using percentage-root-mean square difference (PRD), compression ratio (CR), and quality score (QS). The obtained results from the proposed method are CR=17.2553, PRD=2.9360, and QS=6.4354.

In this paper, the technique of quasi-lossless compression based on the image restoration is presented. The technique of compression described in the paper includes three steps, namely bit compression, correlation removing and image restoration based on the theory of modulation transfer function (MTF). The quasi-lossless compression comes to a high speed. The quality of the reconstruction image under restoration is up to par of the quasi-lossless with higher compression ratio. The experiments of the TM and SPOT images show that the technique is reasonable and applicable.

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.‎

Uncompressed form of the digital images are needed a very large storage capacity amount, as a consequence requires large communication bandwidth for data transmission over the network. Image compression techniques not only minimize the image storage space but also preserve the quality of image. This paper reveal image compression technique which uses distinct image coding scheme based on wavelet transform that combined effective types of compression algorithms for further compression. EZW and SPIHT algorithms are types of significant compression techniques that obtainable for lossy image compression algorithms. The EZW coding is a worthwhile and simple efficient algorithm. SPIHT is an most powerful technique that utilize for image compression depend on the concept of coding set of wavelet coefficients as zero trees. The proposed compression algorithm that combined dual image compression techniques (DICT) invest an excellent features from each methods, which then produce promising technique for still image compression through minimize bits number that required to represent the input image, to the degree allowed without significant impact on quality of reconstructed image.
 The experimental results present that DICT will improve the image compression efficiency between 8 to 24%, and will result high performance of metrics values.

In this paper, a novel data scaling method for pipelined FFT processors is proposed. By using data scaling, the FFT processor can operate on a wide range of input signals without performance loss. Compared to existing block scaling methods, like implementations of Convergent Block Floating Point (CBFP), the memory requirements can be reduced while preserving the SNR. The FFT processor has been synthesized and sent for fabrication in a 0.35μm standard CMOS technology. In netlist simulations, the FFT processor is capable of calculating a 2048 complex point FFT or IFFT in 27μs with a maximum clock frequency of 76MHz.

This paper presents an optimal technique for image compression by which one can achieve maximal storage and transmission capabilities. Realizing image compression hardware into a Field Programmable Gate Array (FPGA) reduces the implementation cost as well as the prototyping and design time cycle. Aggressive algorithm to architecture mapping of a Quad Tree based image compression method produces a fast, compact pipelined design. Mapped into a Xilinx Spartan II, XC200S-5PQ208 FPGA, this design utilizes as much as half the resources that other algorithms such as Discrete Cosine Transform and Wavelet Transform use. Performance of this implementation exceeds those of similar and or greater resource size, and allows for real-time image compression of an image up to 256 pixels by 256 pixels at 256 levels of gray. It is found that the algorithm provides a high compression ratio ranging between 0.12 and 0.68.

In recent times, transmission of information over wireless channels is increasing at an exponential rate. Internet is major source of information; it can be in the form of video or images which alone size up to 72% of the global traffic. In order to tackle such immense data, available channel may not be enough for transmission or reception, in this regard it is imperative to use efficient compressions techniques to reduce its size. Compressed image quality depends on the Quantization Table performed after spatial transformation like Discrete Cosine Transform. Size of a raw image captured by Digital Single-Lens Reflex, having all of its traits can easily exceed 20 Mega Bytes. In proposed compression algorithm, a Crisp parameter p modification step is introduced for effective compression of an image by utilizing standard Joint Picture Expert Group Quantization Table as a baseline model. After implementation of the proposed algorithm, Mean Opinion Score is obtained from the masses through an online survey and it provide the scores of 47.633 at p = 1, 62.74 at p = 8, and 83.252 at p = 16. According to Mean opinion score, best trade-off between quality and size of an image is between the values of p ranges from 11-20, this is also proved by Mean Squared Error and Peak Signal to Noise Ratio, as their ranges are 0.00038-0.000301 and 34.09-34.92 dB respectively. Compression Ratio which is from 6.49-5.76 is also acceptable for the given range.

In the scientific domain, extremely large amounts of data are generated by large-scale high performance computing (HPC) simulations. Storing and sending such vast volumes of data poses serious scalability and performance issues, which can be considerably mitigated by data compression techniques which significantly reduced storage size and data movement burdens. Since scientific data are being shared by scientists more and more frequently, data security methods that ensure the confidentiality, integrity, and availability of data itself are becoming increasingly important. As such, combing compression and encryption is critical to storing large-scale datasets securely. In this work, we explore how to integrate data compression and cryptography techniques as efficiently as possible for big scientific datasets in the HPC field. We perform thorough experiments using different scientific datasets with the state-of-the-art error-bounded lossy compressor - SZ - on a real-world supercomputing environment. Experiments verify that performing encryption before lossy compression (a.k.a., encr-cmpr method) may invalidate the advantage of compression algorithms. By contrast, executing encryption after lossy compression (a.k.a., cmpr-encr method) keeps not only high compression ratios but high overall execution speed. Experiments also verify that the encryption overhead under the cmpr-encr method decreases with increasing compression ratios, which means very good scalability.

<p>The projection into 2030 of the climate data volume increase brings an important challenge to the climate science community. This is particularly true for the CMIP7 that is projected to need about an Exabyte of storage capacity. Error-bounded lossy compression is explored as a potential solution to the above problem by different climate research teams. Several lossy compression schemes have been proposed leveraging different forms of decorrelation (transforms, prediction, HoSVD, DNN), quantization (linear, non-linear, vector), and encoding (dictionary-based, variable length, etc.) algorithms. Our experience with different applications shows that the compression methods often need to be customized and optimized to fit the specificities of the datasets to compress and the user requirements on the compression quality, ratio, and throughput. However, none of the existing lossy compression software for scientific data has been designed to be customizable. To address this issue, we developed SZ3, an innovative customizable, modular compression framework. SZ3 is a full C++ refactoring of SZ2 enabling the specialization, addition, or removal of each stage of the lossy compression pipeline to fit the specific characteristics of the datasets to compress and the use-case requirements. This extreme flexibility allows adapting SZ3 to many different use-cases, from ultra-high compression for visualization to ultra-high-speed compression between the CPU (or GPU) and the memory. Thanks to its unique set of features: customization, high compression ratio, high compression throughput, and excellent accuracy preservation, SZ3 won a 2021 R&D100 award. In this presentation, we present SZ3 and a new data prediction-based decorrelation method that significantly improves the compression ratios for climate datasets over the state-of-the-art lossy compressors, while preserving the same data accuracy. Experiments based on CESM datasets show that SZ3 can lead to up to 300% higher compression ratios than SZ2 with the same compression error bound and similar compression throughput.</p>

The authors' goal was to explore the impact of image compression algorithm and ratio, image luminance, and viewing distance on radiologists' perception of reconstructed image fidelity. Five radiologists viewed 16 sets of four hard-copy chest radiographs prepared for secondary interpretation. Each set included one uncompressed, and three compressed and reconstructed images prepared using three different algorithms but the same compression ratio. The sets were prepared using two subjects, four compression ratios (10:1, 20:1, 30:1, 40:1), and two luminance levels (2,400 cd/m2, standard lightbox illumination, and 200 cd/m2, simulating a typical CRT display). Readers ranked image quality and evaluated obviousness and clinical importance of differences. Viewing distances for image screening, inspection, and comparison were recorded. At 10:1 compression, the compressed and uncompressed images were nearly indistinguishable; the three algorithms were very similar, and differences were rated "not obvious" and "not important." At higher compression, readers consistently preferred uncompressed images, with notable differences between algorithms. The obviousness and clinical importance of differences were rated higher at lightbox luminance. Viewing distances appeared to be idiosyncratic.

The future of healthcare delivery systems and tele medical applications will undergo a radical change due to the developments in wearable technologies, medical sensors, mobile computing and communication techniques. E-health was born with the integration of networks and telecommunications when dealing with applications of collecting, sorting and transferring medical data from distant locations for performing remote medical collaborations and diagnosis. Energy requirement is central to many battery-constrained systems, but they are essential in body sensor networks (BSNs) due to energy and resource constraints, and the critical nature of many healthcare applications. On-node signal processing and compression techniques can save energy by greatly reducing the amount of data transmitted over the wireless channel, but lossy techniques can incur a reduction in application. In this paper we are proposing an aggregation of Deflate and RLE algorithm called Aggregated Deflate RLE (ADR). ADR compression algorithm which provides better compression ratio with minimum compression time as compared to other existing compression techniques like LZW, Huffman and Deflate. We provide here various comparative result of our algorithm with above compression techniques which proves that we are providing best technique for compression of body sensor network data.

Magnetic resonance imaging (MRI) and computer tomography (CT) are very important techniques that are used in disease diagnosis in medicine. In an average sized hospital, many tera-bytes of digital imaging data (MRI and CT) are generated every year, almost all of which has to be kept and archived. The compression of medical images is currently performed by using different algorithms. The most common compression technique is vector quantization. Interframe coding, the discrete Hartley transform, mixed transform, entropy-coded DPCM and JPEG algorithm are also used. Hierarchical finite-state vector quantization (HFSVQ), which is the improved version of vector quantization, has been introduced as a compression technique with high compression ratios for video images. Although the HFSVQ algorithm is the most efficient compression technique according to its compression ratios, we show that it has also a good performance for brain tomography and magnetic resonance images.

The soft computing algorithms are being nowadays used for various multi input multi output complicated non linear control applications. This paper presented the development and implementation of back propagation of multilayer perceptron architecture developed in FPGA using VHDL. The usage of the FPGA (Field Programmable Gate Array) for neural network implementation provides flexibility in programmable systems. For the neural network based instrument prototype in real time application. The conventional specific VLSI neural chip design suffers the limitation in time and cost. With low precision artificial neural network design, FPGA have higher speed and smaller size for real time application than the VLSI design. The challenges are finding an architecture that minimizes the hardware cost, maximizing the performance, accuracy. The goal of this work is to realize the hardware implementation of neural network using FPGA. Digital system architecture is presented using Very High Speed Integrated Circuits Hardware Description Language (VHDL)and is implemented in FPGA chip. MATLAB ANN programming and tools are used for training the ANN. The trained weights are stored in different RAM, and is implemented in FPGA. The design was tested on a FPGA demo board. Keywords- Backpropagation, field programmable gate array (FPGA) hardware implementation, multilayer perceptron, pressure sensor, Xilinx FPGA.

The paper describes the design of 2D-discrete cosine transform (DCT) which is widely used in image and video compression algorithms. The objective of this paper is to design a fully parallel distributed arithmetic (DA) architecture for 2D-dimensional DCT to be implemented on field programmable gate array (FPGA). DCT requires large amount of mathematical computations including multiplications and accumulations. The multipliers consume increased power and area; hence multipliers are completely discarded in the proposed design. Distributed arithmetic is a method of modification at bit stream for sum of product or vector dot product to hide the multiplications. DA is very much suitable for FPGA designs as it reduces the size of a multiply and accumulate hardware. The speed is increased in the proposed design with the fully parallel approach. In this work, existing DA architecture for 2D-DCT and the proposed area efficient fully parallel DA architecture for 2D-DCT are realized. The simulation and synthesis is performed using Xilinx ISE.

This paper outlines a transparent and nonintrusive checkpointing mechanism for use with FPGA-based digital mockups. A digital mockup is an executable model of a physical system and used for real-time test and validation of cyber-physical devices that interact with the physical system. These digital mockups are typically defined in terms of a large set of ordinary differential equations. We consider digital mockups impelemented on field-programmable gate arrays (FPGAs). A checkpoint is a snapshot of the internal state of the model at a specific point in time as captured by some controller that resides on the same FPGA. We require that the model continues uninterrupted execution during a checkpointing operation. Once a checkpoint is created, the corresponding state information is transferred from the FPGA to a host computer for visualization and other off-chip processing. We outline the architecture of a checkpointing controller that captures and transfers the state information at a desired clock cycle using an aggressive compression technique. Our compression technique achieves 90% reduction in data transferred from the FPGA to the host computer under periodic checkpointing scenarios. The checkpointing with compression yields 15-36% FPGA size overhead, versus 6-11% for checkpointing without compression.