Compression Architecture Research Articles

Implementation of a novel adaptive coding using VLSI architecture for data compression in image processing

Implementation of a novel adaptive coding using VLSI architecture for data compression in image processing

An FPGA Accelerator for Real Time Hyperspectral Images Compression based on JPEG2000 Standard

Lossless hyperspectral images have the advantage of reducing the data size, hence saving on storage and transmission costs. This study presents a dynamic pipeline hardware design for compressing and decompressing images using the Joint Photographic Experts Group-Lossless (JPEG2000) algorithm. The proposed architecture was specifically tailored for implementation on a Field Programmable Gate Array (FPGA) to accomplish efficient image processing. The introduction of a pipeline pause mechanism effectively resolves the issue of coding errors deriving from parameter modifications. Bit-plane coding was employed to enhance the efficacy of image coding calculations, leading to a reduction of parameter update delays. However, the context and decision creation procedure were streamlined, resulting in a significant enhancement in throughput. A hardware module utilizing the parallel block compression architecture was developed for JPEG2000 compression/decompression, allowing for configurable block size and bringing about enhanced image, compression/decompression, throughput, and reduced times. Verification results were obtained by implementing the proposed JPEG 2000 compression on a Zynq-7000 system-on-chip. The purpose of this system was to enable on-board satellite processing of hyperspectral image cubes with a specific focus on achieving lossless compression. The proposed architecture outperformed previous approaches by using fewer resources and achieving a higher compression ratio and clock frequency.

An energy efficient reconfigurable architecture for multi-lead ECG signal compression

An energy efficient reconfigurable architecture for multi-lead ECG signal compression

Improved DWT and IDWT architectures for image compression

In the recent era, a rapid development in the field of image processing has been observed. One of the important applications in image processing is compression. Several wavelet transform based image compression techniques have already been introduced. In this paper, Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT) based improved image compression and decompression techniques have been proposed by incorporating a scaling factor. The DWT and IDWT algorithms are implemented using folded architecture. To reduce the usages of hardware resources, a multiplier is recursively used. Image compression and decompression schemes based on proposed DWT and IDWT architectures are tested using four different image databases. The proposed technique provides better results in terms of bits per pixel, compression ratio, mean square error, peak-signal-to-noise ratio, normalized correlation coefficient and structural similarity index. FPGA based synthesis has been performed using Xilinx Vivado Synthesis tool in terms of slice LUTs, slice registers, clock frequency, delay and power. The synthesis results show that proposed DWT and IDWT architectures are amenable for image compression and decompression applications.

Fast and Efficient Entropy Coding Architectures for Massive Data Compression

The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented—more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems.

A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm

A 2D Discrete Cosine Transform and Inverse Discrete Cosine Transform using the B.G. Lee algorithm, incorporating a signed error-tolerant adder for additions, and a signed low-power fixed-point multiplier to perform multiplications are proposed and designed in this research. A novel Application Specific Integrated Circuit hardware implementation is used for the 2D DCT/IDCT computation of each 8 × 8 image block by optimizing the input data using the concepts of pipelining. An enhanced speed in processing and optimized arithmetic computations was observed due to the eight-stage pipeline architecture. The 2D DCT/IDCT of each 8 × 8 image segment can be quickly processed in 34 clock cycles with a substantially reduced level of circuit complexity. The B.G. Lee algorithm has been implemented using signed error-tolerant adders, signed fixed-point multipliers, and shifters, reducing computational complexity, power, and area. The Cadence Genus tool synthesized the proposed architecture with gpdk-90 nm and gpdk-45 nm technology libraries. The proposed method showed a significant reduction of 31.01%, 12.17%, and 21.11% in power, area, and PDP in comparison to the existing image compression architectures. An improved PSNR of the reconstructed image was also achieved compared to existing designs.

Proposed SAR range focusing algorithm based on simulation analysis and SDR implementation

Synthetic aperture radar (SAR) imaging systems use pulse focusing techniques for achieving fine range resolution and large range detection. Stretch processing and correlation pulse focusing techniques are the most popular algorithms used in the radar signal processing stage. The architecture of the stretch processing technique makes it commonly used in frequency modulated continuous wave (FMCW) radar systems due to its simple architecture, low sampling rate, and inexpensive hardware. This paper discusses how the stretch range focusing approach can be used instead of the matched filter technique in the processing stage of the SAR Range-Doppler algorithm (RDA) to generate images of finer resolution and higher quality. In addition, validating the proposed algorithm and evaluating its performance according to some evaluation indices such as peak sidelobe ratio (PSLR), integrated sidelobe ratio (ISLR), and the impulse response width (IRW). Also, this paper presents a comprehensive analysis and hardware implementation of the two SAR algorithms, based on software-defined radio (SDR) architecture where it is characterized by less complexity, low cost, and high flexibility due to the usage of both software data processing and software waveform generation. Two Universal Software Radio Peripheral (USRP-2932) and NI-LabVIEW® software development environment are the platforms used for the hardware validation process. The transmitted waveform is a linear frequency modulated (LFM) signal generated by the first USRP (transmitter) which will hit a target located at a certain distance. The second USRP is used as a receiver for target’s echo processing and detection. This processing stage contains two architectures, which are the matched filter pulse compression architecture and the stretch range focusing approach. Finally, the validation process of the hardware results is performed based on the pre-mentioned evaluation indices.

An efficient image compression architecture based on optimized 9/7 wavelet transform with hybrid post processing and entropy encoder module

Satellite communication is popular due to the advancement in storing and transmitting satellite images. Image compression can be defined as the science of lowering the number of bits necessary to represent an image. In satellite image processing, image degradation is considered a challenging problem. The Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) standard CCSDS-122.0-B-1 is the transformation-dependent image compression method developed especially for usage in on-board space platforms. It holds de-correlation, quantization and entropy encoding phases. This paper introduces a new optimized memory organization for Discrete Wavelet Transform (DWT) to perform spatial de-correlation with fewer memory requirements on an FPGA device. Also, the proposed optimized DWT is integrated with a hybrid post-processing and entropy encoder module to reduce the spatial redundancies between the wavelet coefficients and compress the de-correlated data with high compression performance. A high-throughput hardware implementation of the Binary arithmetic entropy Coder (BAEC) is also provided to perform lossless compression with low implementation complexity. To provide the convenience and compactness, the proposed system will be implemented in the Xilinx working platform by developing a Verilog code. The proposed model is then evaluated on the Arty Z7–20 development board. The proposed approach is analyzed on different performance parameters such as throughput and frequency. The proposed design allowed a maximum operating frequency of 250 MHz, leading to a throughput of 156.25 Msamples/sec on Zynq. In addition, the structure of the proposed method overtakes the conventional design in terms of memory requirements and area.

Edge-Guided Hyperspectral Image Compression With Interactive Dual Attention

Compressing hyperspectral images (HSIs) into compact representations under the premise of ensuring high-quality reconstruction is an essential task in HSI processing. However, existing compression methods usually encode images by smoothing due to the low-frequency information occupying a prominent component in most images. Consequently, these methods fail to capture sufficient structural information, especially in low bit rates, often causing inferior reconstruction. To address this problem, we propose here an edge-guided hyperspectral compression network, called CENet, to realize high-quality reconstruction. To enhance the structural latent representation ability, the CENet model incorporates an edge extractor neural network into the compression architecture to guide compression optimization by the edge-guided loss. We propose an interactive dual attention module to selectively learn edge features, obtain the most effective edge structure, and avoid additional edge information redundancy at the same time. In the proposed CENet, the edge-guided loss and interactive dual attention module are combined to enhance the comprehensive structure of the latent representation. Concretely, interactive dual attention makes the edge extraction network focus only on moderate boundaries rather than on all edges, which enables savings on the bit rate cost and helps achieve a strong structural representation. As a result, the reconstruction quality is significantly improved. The extensive experiments on seven HSI datasets verify that our model can effectively raise the rate–distortion performance for HSIs of any type or resolution (e.g., yielding an average peak signal-to-noise ratio (PSNR) of 30.59 dB at 0.2382 bpppb, which exceeds the baseline for Chikusei by 10.99%).

Guest Editorial: Low Power Architectures for Digital Video and Image Compression

Guest Editorial: Low Power Architectures for Digital Video and Image Compression

Time-Invariant Prefix Coding for LQG Control

Motivated by control with communication constraints, in this work we develop a time-invariant data compression architecture for linear–quadratic–Gaussian (LQG) control with minimum bitrate prefix-free feedback. For any fixed control performance, the approach we propose nearly achieves known directed information (DI) lower bounds on the time-average expected codeword length. We refine the analysis of a classical achievability approach, which required quantized plant measurements to be encoded via a time-varying lossless source code. We prove that the sequence of random variables describing the quantizations has a limiting distribution and that the quantizations may be encoded with a fixed source code optimized for this distribution without added time-asymptotic redundancy. Our result follows from analyzing the long-term stochastic behavior of the system, and permits us to additionally guarantee that the time-average codeword length (as opposed to expected length) is almost surely within a few bits of the minimum DI. To our knowledge, this time-invariant achievability result is the first in the literature.

Flow-MotionNet: A neural network based video compression architecture

Flow-MotionNet: A neural network based video compression architecture

Flow incorporated neural network based lightweight video compression architecture

The sudden surge in the video transmission over internet motivated the exploration of more promising and potent video compression architectures. Though the frame prediction based hand designed techniques are performing well and widely used but the recent deep learning based researches in this domain provided further directions of pure deep learning based next generation codecs. As the bandwidth over the internet is varying, adaptive bit rate representation is more suitable for video quality adjustment in tune with bandwidth variation. The proposed architecture comprises of end to end trainable video compression network consisting of majorly three modules namely-motion extension network, flow autoencoder and frame autoencoder. Frame autoencoder generates the individual compressed frames, flow autoencoder is used for optical flow based motion compensation chore and next frame is predicted by the motion extension network. The network is designed and evaluated in incremental manner. The analysis of the outcomes demonstrates the promising performance of the network quantitatively and qualitatively. Moreover, the results reveal that inclusion of optical flow based motion compensation network to the MotionNet architecture has enhanced the performance.

Savitzky Golay and KPCA based Optimal Discrete Wavelet Transform Architecture for Image Compression

• In pre-processing, Savitzky Golay filtering technique (SGFT) is utilized to eliminate the noise signals thereby generating accurate results. • Utilizing DWT along with Kernel principal component analysis and Iterative artificial ecosystem optimization algorithm for post-processing thus obtaining an optimal reconstructed image with high quality. • Comparing our proposed approach with various other techniques to determine the effectiveness of the system. In recent years, the DWT acts as an effective tool in image compression and decomposition. There exist numerous transform techniques for compressing images but there occurs information loss and low picture quality. To overcome such drawbacks this paper aims in developing an optimal reconstructed image with high quality. The proposed approach comprises two significant phases namely the pre-processing and post-processing. During pre-processing, the multispectral images are enhanced by removing the noise signals from the input image. In this paper, a Savitzky Golay filter is employed to eliminate noise signals and smoothing the multi-spectral image for the generation of more accurate results. The pre-processed image is then subjected to post-processing. In post-processing, the DWT along with Kernel principal component analysis and Iterative artificial ecosystem optimization algorithm is employed to obtain an optimal reconstructed image with high quality and without losing the information. In addition to this, the proposed model is trained using aerial images obtained from the SIPI image database. The simulation metrics are evaluated for various approaches and the results reveal that the proposed approach outperforms other approaches.

Learning to compress videos without computing motion

Video has become an increasingly important part of our daily digital communication. With the development of higher resolution contents and displays, its significant volume poses significant challenges to the goals of acquiring, transmitting, compressing and displaying high quality video content. In this paper, we propose a new deep learning video compression architecture that does not require motion estimation, which is the most expensive element of modern hybrid video compression codecs like H.264 and HEVC. Our framework exploits the regularities inherent to video motion, which we capture by using displaced frame differences as video representations to train the neural network. In addition, we propose a new space–time reconstruction network based on both an LSTM model and a UNet model, which we call LSTM-UNet. The combined network is able to efficiently capture both temporal and spatial video information, making it highly amenable for our purposes. The new video compression framework has three components: a Displacement Calculation Unit (DCU), a Displacement Compression Network (DCN), and a Frame Reconstruction Network (FRN), all of which are jointly optimized against a single perceptual loss function. The DCU removes the need for motion estimation found in hybrid codecs, and is less expensive. In the DCN, an RNN-based network is utilized to compress displaced frame differences as well as retain temporal information between frames. The LSTM-UNet is used in the FRN to learn space time differential representations of videos. Our experimental results show that our compression model, which we call the MOtionless VIdeo Codec (MOVI-Codec), learns how to efficiently compress videos without computing motion. Our experiments show that MOVI-Codec outperforms the Low-Delay P (LDP) veryfast setting of the video coding standard H.264 and exceeds the performance of the modern global standard HEVC codec, using the same setting, as measured by MS-SSIM, especially on higher resolution videos. In addition, our network outperforms the latest H.266 (VVC) codec at higher bitrates, when assessed using MS-SSIM, on high resolution videos. The MOVI-Codec project page can be found at https://github.com/Meixu-Chen/MOVI-Codec.

Satellite Image Compression and Denoising With Neural Networks

Earth observation through satellite images is crucial to help economic activities as well as to monitor the impact of human activities on ecosystems. Current satellite systems are subjected to strong computational complexity constraints. Thus, image compression is performed onboard with specifically tailored algorithms while image denoising is performed on the ground. In this letter, we intend to address satellite image compression and denoising with neural networks. The first proposed approach uses a single neural architecture for joint onboard compression and denoising. The second proposed approach sequentially uses a first neural architecture for onboard compression and a second one for on ground denoising. For both approaches, the onboard architectures are lightened as much as possible, following the procedure proposed by Alves de Oliveira <i>et al.</i> (2021). The two approaches are shown to outperform the current satellite imaging system and their respective pros and cons are discussed.

An energy efficient reconfigurable architecture for multi-lead ECG signal compression

An energy efficient reconfigurable architecture for multi-lead ECG signal compression

Identification of Radiological and Clinical Factors that Increase the Risk of Chronic Subdural Hematoma Recurrence.

To determine risk factors predicting chronic subdural hematoma (CSH) recurrence is a common type of intracranial hemorrhage in elderly patients. Despite improved medical diagnosis and treatment, the reoperation rate remains high. A retrospective review of 291 CSH patients admitted to our department was performed. Clinical and radiological factors predictive for CSH recurrence were identified by univariate analyses; variables whose p-value was < 0.05 underwent multivariate logistic regression analyses. Univariate analyses revealed that preoperative midline shift (p=0.025), mix-density hematoma (p=0.023), internal architecture of hematoma (p=0.044), membranectomy (p=0.001), and ambient cistern compression (p=0.001) correlated with a significantly higher rate of recurrence. Multivariate analyses showed that separated architecture, membranectomy and ambient cistern compression were independent risk factors for CSH recurrence. Among many factors, membranectomy, separated architecture, and ambient cistern compression were the strongest predictors for recurrence.

A Joint Spatiotemporal Video Compression Based on Stochastic Adaptive Fourier Decomposition

This paper proposes a novel video compression method &#x2013; stochastic adaptive Fourier decomposition (SAFD) based joint spatiotemporal model (JSTM). SAFD is a recently developed sparse representation theory that combines the traditional signal decomposition method with machine learning to adaptively decompose multi-signals into common atoms of predefined interpretable Szeg<inline-formula><tex-math notation="LaTeX">$\ddot{\text{o}}$</tex-math></inline-formula> kernel dictionary. Based on SAFD, this paper firstly proposes a learning-based 3D video to 2D data embedding method. The method learns common atoms based on the frames themselves that require to be compressed, without the need for pre-training on large-scale data like deep learning. Then the embedding-based JSTM and a feasible video compression architecture are developed. The contribution is twofold: introducing SAFD into video compression first time in the literature and developing a new video compression model JSTM. The experimental results are promising.

Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN Autoencoder.

This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts (MA), introduced by the movement of muscles. The existing EEG denoising methods make use of decomposition, thresholding and filtering techniques. In the proposed approach, EEG signals are first transformed to orthogonal domain using Tchebichef moments before feeding to the proposed architecture. A new hyper-parameter ( α ) is introduced which refers to the fractional order with respect to which gradients are calculated during back-propagation. It is observed that by tuning α , the quality of the restored signal improves significantly. Motivated by the high usage of portable low energy devices which make use of compressed deep learning architectures, the trainable parameters of the proposed architecture are compressed using randomized singular value decomposition (RSVD) algorithm. The experiments are performed on the standard EEG datasets, namely, Mendeley and Bonn. The study shows that the proposed fractional and compressed architecture performs better than existing state-of-the-art signal denoising methods.