Adaptive Compression Algorithm Research Articles

Does amplitude compression help or hinder attentional neural speech tracking?

Amplitude compression is an indispensable feature of contemporary audio production and especially relevant in modern hearing aids. The cortical fate of amplitude-compressed speech signals is not well-studied, however, and may yield undesired side effects: We hypothesize that compressing the amplitude envelope of continuous speech reduces neural tracking. Yet, leveraging such a 'compression side effect' on unwanted, distracting sounds could potentially support attentive listening if effectively reducing their neural tracking. In this study, we examined 24 young normal-hearing (NH) individuals, 19 older hearing-impaired (HI) individuals, and 12 older normal-hearing individuals. Participants were instructed to focus on one of two competing talkers while ignoring the other. Envelope compression (1:8 ratio, loudness-matched) was applied to one or both streams containing short speech repeats. Electroencephalography (EEG) allowed us to quantify the cortical response function and degree of speech tracking. With compression applied to the attended target stream, HI participants showed reduced behavioural accuracy, and compressed speech yielded generally lowered metrics of neural tracking. Importantly, we found that compressing the ignored stream resulted in a stronger neural representation of the uncompressed target speech. Our results imply that intelligent compression algorithms, with variable compression ratios applied to separated sources, could help individuals with hearing loss suppress distraction in complex multi-talker environments.Significant statement Amplitude compression, integral in contemporary audio production and hearing aids, poses an underexplored cortical challenge. Compressing the amplitude envelope of continuous speech is hypothesized to diminish neural tracking. Yet, capitalizing on this 'compression side effect' for distracting sounds might enhance attentive listening. Studying normal-hearing (NH), older hearing-impaired (HI), and older normal hearing individuals in dual-talker scenarios, we applied envelope compression to speech streams. Both NH and HI participants showed diminished neural tracking with compression on the speech streams. Despite weaker tracking of a compressed distractor, HI individuals exhibited stronger neural representation of the concurrent target. This suggests that adaptive compression algorithms, employing variable ratios for distinct sources, could aid individuals with hearing loss in suppressing distractions in complex multi-talker environments.

Adaptive Compression and Reconstruction for Multidimensional Medical Image Data: A Hybrid Algorithm for Enhanced Image Quality.

Spatial regions within images typically hold greater priority over adjacent areas, especially in the context of medical images (MI) where minute details can have significant clinical implications. This research addresses the challenge of compressing medical image dimensions without compromising critical information by proposing an adaptive compression algorithm. The algorithm integrates a modified image enhancement module, clustering-based segmentation, and a variety of lossless and lossy compression techniques. Edge enhancement contrast limited adaptive histogram equalization (EE-CLAHE) and 2D adaptive anisotropic diffusion filter are employed to enhance and denoise the images, followed by adaptive expectation maximization clustering (AEMC) for segmentation into regions of interest (ROI) and non-ROI. The clustering process is optimized utilizing fuzzy c-means (FCM) and Otsu thresholding. Subsequently, distinct compression schemes are applied to ROI and non-ROI regions, such as Coiflet + Haar, Coiflet + Daubecheis, modified SPIHT Huffman, EZW, and SPIHT algorithms, to ensure effective storage and transmission while preserving diagnostic details. Experimental results demonstrate that the combination of the modified SPIHT Huffman algorithm for ROI and EZW for non-ROI yields superior reconstruction quality across various measures, enabling comprehensive analysis of multi-dimensional images from MRI, CT, and X-ray modalities.

Construction of Artistic Design Patterns Based on Improved Distributed Data Parallel Computing of Heterogeneous Tasks

With the continuous upgrading of hardware in the terminal equipment, how to provide high-performance computing for low-tech threshold users has become a current research hotspot. In the era of green high-performance computing, the heterogeneous computing system can provide good versatility, performance, and efficiency and has broad development prospects. This article provides an in-depth analysis and research on the construction and application of improved models using the artistic design pattern of heterogeneous tasks and parallel computing. Based on the hardware resources in the existing desktop system, this article optimizes the original heterogeneous parallel technology from the aspects of task division and data transmission to reduce the complexity of data allocation and processing for users. Based on the analysis and study of the multicore CPU and GPU architectures in the desktop system, as well as the original CPU-GPU heterogeneous parallel technology, this article optimizes the solution of heterogeneous parallel computing, designs a heterogeneous parallel computing architecture, and deploys a heterogeneous parallel computing architecture. The nodes of the desktop system constitute the parallel computing system. In terms of task allocation, the computing system divides tasks according to the parallelism of tasks. According to the computing resources and bandwidth conditions of each heterogeneous node, starting from the parallel execution time, the task scheduling algorithm is optimized, and the load balancing scheduling scheme is designed to achieve the optimal allocation of resources. In terms of storage resources, the computing system adopts distributed storage as a whole. The CPU-GPU heterogeneous parallel in the desktop system adopts virtual unified storage. Global distributed storage and local shared storage are used to balance overall performance and programming complexity. This article introduces the design and implementation of JTangSync, a distributed heterogeneous data synchronization system. The system adopts a distributed architecture, and each node is organized by a data source module, a data transmission module, a processor module, etc. The data source module is responsible for extracting data, the data transmission module is mainly responsible for efficient data transmission, and the processor module is responsible for data processing. More importantly, each module is designed as a replaceable plug-in, which is convenient for secondary expansion. Each node relies on ZooKeeper to form a cluster, which realizes distributed functions such as centralized management of distributed resources, failover, and resumed transmission. Compared with the mainstream scheduling algorithms HEFT, CPOP, PEFT, and HSIP on heterogeneous systems participating in the experimental evaluation, the scheduling length ratio of DONF series algorithms is reduced by 36.3%–67.5% and the parallelism is increased by 17%–125% in terms of efficiency. Compared with the existing database synchronization system, the JTangSync system has built-in multiple heterogeneous database data sources and supports the synchronization of complex heterogeneous databases. The system supports users to develop and customize their own data sources and data processing programs, to promote secondary development. By adopting the custom compressed data exchange format and network optimization methods such as packet merging, caching, and adaptive compression algorithm, the system has high performance.

Effects of Adaptive Non-linear Frequency Compression in Hearing Aids on Mandarin Speech and Sound-Quality Perception

ObjectiveThis study was aimed at examining the effects of an adaptive non-linear frequency compression algorithm implemented in hearing aids (i.e., SoundRecover2, or SR2) at different parameter settings and auditory acclimatization on speech and sound-quality perception in native Mandarin-speaking adult listeners with sensorineural hearing loss.DesignData consisted of participants’ unaided and aided hearing thresholds, Mandarin consonant and vowel recognition in quiet, and sentence recognition in noise, as well as sound-quality ratings through five sessions in a 12-week period with three SR2 settings (i.e., SR2 off, SR2 default, and SR2 strong).Study SampleTwenty-nine native Mandarin-speaking adults aged 37–76 years old with symmetric sloping moderate-to-profound sensorineural hearing loss were recruited. They were all fitted bilaterally with Phonak Naida V90-SP BTE hearing aids with hard ear-molds.ResultsThe participants demonstrated a significant improvement of aided hearing in detecting high frequency sounds at 8 kHz. For consonant recognition and overall sound-quality rating, the participants performed significantly better with the SR2 default setting than the other two settings. No significant differences were found in vowel and sentence recognition among the three SR2 settings. Test session was a significant factor that contributed to the participants’ performance in all speech and sound-quality perception tests. Specifically, the participants benefited from a longer duration of hearing aid use.ConclusionFindings from this study suggested possible perceptual benefit from the adaptive non-linear frequency compression algorithm for native Mandarin-speaking adults with moderate-to-profound hearing loss. Periods of acclimatization should be taken for better performance in novel technologies in hearing aids.

Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks.

The healthcare sector is currently undergoing a major transformation due to the recent advances in deep learning and artificial intelligence. Despite a significant breakthrough in medical imaging and diagnosis, there are still many open issues and undeveloped applications in the healthcare domain. In particular, transmission of a large volume of medical images proves to be a challenging and time-consuming problem, and yet no prior studies have investigated the use of deep neural networks towards this task. The purpose of this paper is to introduce and develop a deep-learning approach for the efficient transmission of medical images, with a particular interest in the progressive coding of bit-planes. We establish a connection between bit-plane synthesis and image-to-image translation and propose a two-step pipeline for progressive image transmission. First, a bank of generative adversarial networks is trained for predicting bit-planes in a top-down manner, and then prediction residuals are encoded with a tailored adaptive lossless compression algorithm. Experimental results validate the effectiveness of the network bank for generating an accurate low-order bit-plane from high-order bit-planes and demonstrate an advantage of the tailored compression algorithm over conventional arithmetic coding for this special type of prediction residuals in terms of compression ratio.

On Complexity of Adaptive Splines

The paper discusses various methods of adaptive spline approximations for the flow of function values. It is considered an adaptive compression algorithm, which, for a priori given , has the properties 1) the complexity of the algorithm is proportional to the length of the original flow, 2) by the piecewise linear interpolation of the compression result, it is possible to restore the original flow with an accuracy of 3) the compression result is close to optimal and has 0(M) of arithmetic operations. The effectiveness of this approach is demonstrated on rapidly changing initial flows of numerical information in the digital experiment . In addition, the paper presents an exact two-sided estimate for the number 0(M2 ) of arithmetic operations for the optimal solution of the problem of compressing an informational numerical flow of length M with the possibility of recovering this flow with a predetermined accuracy. Provided that the original flow is convex, a compression algorithm is developed with an accurate twosided estimate of the number 0(Mlog2M) and with the possibility of recovery with a prescribed accuracy.

The Generalized Haar Spaces and Their Adaptive Decomposition

This paper is devoted to the numericalinformation flows and adaptive decompositions of thegeneral Haar functions connected with them. The aim ofthis paper is to propose an adaptive wavelet decompositionusing an adaptive compression algorithm for a flow ofnumerical information of length M with complexity𝑶(𝑴) and with a given precision of 𝜀> 0. The numericalflows are associated with irregular spline grids. This paperdiscusses the calibration relations, the embedding of thegeneral Haar spaces and their wavelet decompositions.The structure of the decomposition/ reconstructionalgorithms are done. The cases of the finite and the infiniteflows are considered. The paper discusses various methodsof adaptive Haar approximations for the flow of functionvalues. Assuming that the values of the first derivative ofthe approximated function is known (exactly orapproximately), the complexity of using an adaptive grid isestimated for a priori specified approximation accuracy.The number of K knots in the adaptive grid determine therequired amount of memory for storage of thecompression results. The number of M knots of the initialgrid characterizes the number of operations required toobtain the adaptive compression. In the case of access tothe derivative values (or their approximations) thenumber of digital operations is proportional to the numberM. If it does not have access to the last ones then thenumber of required operations has the order of M2 (in thegeneral case). If additionally, the approximated flow isconvex, then the number of required operations has theorder of M log2M. In all cases the result requires thecomputer memory amount to be of the order of K

Quality control method of the transmission of fine image details in the JPEG2000

The article proposes a method for controlling the transmission quality of fine image details in the JPEG2000 standard based on an automatic adjustment of the quantization parameters of discrete wavelet transform (DWT) coefficients. An algorithm for setting up quantization scale parameters for different transformation sub-ranges of the DWT coefficients depending on given (permissible) distortions is described. For an objective assessment of image quality, numerical measures of fine detail distortion in a normalized N-CIELAB colorimetric system are used, according to which an analysis of their structural features is performed. Results of the experimental studies of the analysis of image quality and compression efficiency depending on the quantization parameters utilized in the developed adaptive compression algorithm are presented. Results of evaluating the algorithm performance that can be used for practical applications in multimedia applications are also presented.

Implementation of Text Compression using Adaptive Shannon-Fano Algorithm

This study aims to implement the Shannon-fano Adaptive data compression algorithm on characters as input data. This study also investigates the data compression ratio, which is the ratio between the number of data bits before and after compression. The resulting program is tested by using black-box testing, measuring the number of character variants and the number of types of characters to the compression ratio, and testing the objective truth with the Mean Square Error (MSE) method. The description of the characteristics of the application made is done by processing data in the form of a collection of characters that have different types of characters, variants, and the number of characters. This research presents algorithm that support the steps of making adaptive Shannon-fano compression applications. The length of the character determines the variant value, compression ratio, and the number of input character types. Based on the results of test results, no error occurs according to the comparison of the original text input and the decompression results. A higher appearance frequency of a character causes a greater compression ratio of the resulting file; the analysis shows that a higher number of types of input characters causes a lower compression ratio, which proves that the proposed method in real-time data compression improves the effectiveness and efficiency of the compression process.

CIAPC algorithm forrandom frequency‐hopping radar

In random frequency-hopping pulse–Doppler radar, the transmitted frequencies are randomly distributed in the given frequency bandwidth. As a result, this kind of radar has the advantage of anti-jamming. However, because of the sidelobe level fluctuating randomly, the sidelobe of a large scatterer can mask the presence of nearby smaller scatterers. The adaptive pulse compression (APC) algorithm has been shown capable of effectively suppressing the range sidelobes, but the performance gain is diminished whenever the return signals have a low signal-to-noise ratio (SNR). Furthermore, moving targets with high speed bring about the range migration problem. To solve these problems, the coherent integration APC (CIAPC) algorithm is proposed. The simulation results indicate that in a low SNR scenario, the CIAPC algorithm can suppress the range sidelobes of large scatterers and can improve detection performance.

Application of Sparse Dictionary Adaptive Compression Algorithm in Transient Signals

An adaptive compression algorithm based on sparse dictionary is proposed to solve the pressure on the transmission system caused by long-time high sampling rate sampling of transient signals. Due to the obvious difference between transient and non-transient information features of transient signals. According to the sparse dictionary construction characteristic that the more matching atoms and signal features are, the better sparse performance is, the transient signal feature information is extracted and compressed separately. After data compression, the principle of compressed sensing is applied to restore the transient signal, so as to reduce the amount of data transmitted in the transmission system. In summary, the adaptive sparse dictionary compression algorithm effectively compresses the transient signal data, and the compressed data can accurately recover the transient signal.

49‐4: A Low‐power Reflective LCD System with Adaptive Compression Algorithm of Grey Scale

A 1.28‐inch 6‐bit color low‐power reflective LCD prototype has been developed by using low refresh rate system and memory‐type pixel (MTP) structure. As there is an intrinsic problem for the MTP structure that large flat areas, and loss of detail become noticeable when original 8‐bit RGB color is converted to 2‐bit RGB color. An adaptive compression algorithm of grey scale has been developed to minimize the image deterioration, which enables to select an appropriate parameter according to the types of images and the position on the screen. By a specific function of RGB point‐to‐point gray‐scale transformation, the picture shows better effect than the traditional processing method. The simulation and experimental results verify the effectiveness of the adaptive compression algorithm.

The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*

In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

Analyzing the Effect and Performance of Lossy Compression on Aeroacoustic Simulation of Gas Injector

Computational fluid dynamic simulations involve large state data, leading to performance degradation due to data transfer times, while requiring large disk space. To alleviate the situation, an adaptive lossy compression algorithm has been developed, which is based on regions of interest. This algorithm uses prediction-based compression and exploits the temporal coherence between subsequent simulation frames. The difference between the actual value and the predicted value is adaptively quantized and encoded. The adaptation is in line with user requirements, that consist of the acceptable inaccuracy, the regions of interest and the required compression throughput. The data compression algorithm was evaluated with simulation data obtained by the discontinuous Galerkin spectral element method. We analyzed the performance, compression ratio and inaccuracy introduced by the lossy compression algorithm. The post processing analysis shows high compression ratios, with reasonable quantization errors.

Adaptive block-wise alphabet reduction scheme for lossless compression of images with sparse and locally sparse histograms

We propose a new adaptive block-wise lossless image compression algorithm, which is based on the so-called alphabet reduction scheme combined with an adaptive arithmetic coding (AC). This new encoding algorithm is particularly efficient for lossless compression of images with sparse and locally sparse histograms. AC is a very efficient technique for lossless data compression and produces a rate that is close to the entropy; however, a compression performance loss occurs when encoding images or blocks with a limited number of active symbols by comparison with the number of symbols in the nominal alphabet, which consists in the amplification of the zero frequency problem. Generally, most methods add one to the frequency count of each symbol from the nominal alphabet, which leads to a statistical model distortion, and therefore reduces the efficiency of the AC. The aim of this work is to overcome this drawback by assigning to each image block the smallest possible set including all the existing symbols called active symbols. This is an alternative of using the nominal alphabet when applying the conventional arithmetic encoders. We show experimentally that the proposed method outperforms several lossless image compression encoders and standards including the conventional arithmetic encoders, JPEG2000, and JPEG-LS.

Real-time implementation of an adaptive simultaneous dynamic range compression and local contrast enhancement algorithm on a GPU

Dynamic range compression is a fundamental function required in digital video cameras and display monitors to improve the visual appeal of color images. This paper presents a real-time implementation of an algorithm for adaptive dynamic range compression and contrast enhancement on a graphics processing unit (GPU). To achieve this, we first designed an adaptive intensity transfer function to handle the enhancement of standard-dynamic-range (8 bits/channel) images with both low- and high-intensity. The proposed algorithm then combines the intensity transfer function with an existing simultaneous dynamic range compression and local contrast enhancement (SDRCLCE) algorithm for the simultaneous compression of dynamic range and enhancement of local contrast. To reach real-time performance in processing high-resolution color images, the proposed algorithm is implemented using an NVIDIA GeForce GTX 650 Ti GPU based on NVIDIA’s Compute Unified Device Architecture (CUDA) parallel programming running on a 3.33 GHz Intel Core i5-661 CPU. Compared with a LUT-accelerated implementation, the proposed GPU implementation was shown to accelerate the processing of 1024 × 1024 color images by 11.1 times and color images of 4096 × 4096 pixels by 7.5 times, including the cost of memory copy between the host and device.

An adaptive two-scale enhancement method to visualize man-made objects in very high resolution SAR images

Ocean vessels and aircrafts usually cause large oscillated responses and span a high dynamic range (HDR) in synthetic aperture radar (SAR) images. To visualize and further recognize these objects on common display devices, it is necessary to compress the HDR at the proper ratio and enhance the details at both low and high grey levels. To achieve this goal, an adaptive two-scale enhancement method is proposed. First, to enhance the details at low grey levels, an adaptive global HDR compression algorithm is designed based on the grey-level distribution feature. Then, with a simplified heterogeneity measurement based on the weighted standard deviation and entropy at a local scale, high-backscattering objects are determined and enhanced to highlight their peak details at high grey levels. The proposed method is validated using two different types of HDR SAR images, and the experimental results suggest that the proposed method visualizes the information at all grey levels and enhances the object’s local peaks. Compared with gamma mapping algorithms, which are widely used for SAR image display, the proposed method outperforms in visualizing the details of man-made objects in very high resolution SAR images.

Discriminative Fine-Grained Mixing for Adaptive Compression of Data Streams

This paper introduces an adaptive compression algorithm for transfer of data streams across operators in stream processing systems. The algorithm is adaptive in the sense that it can adjust the amount of compression applied based on the bandwidth, CPU, and workload availability. It is discriminative in the sense that it can judiciously apply partial compression by selecting a subset of attributes that can provide good reduction in the used bandwidth at a low cost. The algorithm relies on the significant differences that exist among stream attributes with respect to their relative sizes, compression ratios, compression costs, and their amenability to application of custom compressors. As part of this study, we present a modeling of uniform and discriminative mixing, and provide various greedy algorithms and associated metrics to locate an effective setting when model parameters are available at run-time. Furthermore, we provide online and adaptive algorithms for real-world systems in which system parameters that can be measured at run-time are limited. We present a detailed experimental study that illustrates the superiority of discriminative mixing over uniform mixing.

An Adaptive Compression Algorithm of Scattered Point Cloud Based on Wavelet Technology

An adaptive compression algorithm of scattered point cloud based on wavelet technology is proposed in this paper. The proposed algorithm converts 3D space point cloud into point sets on a 2D plane by using slicing technology in rapid prototyping theory. After the wavelet transform operation, the wavelet coefficients of the sorted point cloud data are obtained, the peaks of these wavelet coefficients can represent the points to be reserved. According to the experiment, the fast and efficient compression of scattered point cloud can be achieved when the slice thickness is approximately 2 to 3 times the sampling interval. Comparison results between the proposed algorithm and conventional algorithm indicates that the former exhibits obvious advantages in feature reservation. The details and feature information can be ultimately reserved in this algorithm, thus making the compression results ideal. The proposed algorithm does not need to set a threshold to explain its adaptability and realize automatic compression.