Vectorcardiogram (VCG) signal compression is very much in demand in the present-day scenario due to the increasing number of cardiac patients. Hence, in this paper, a new technique is proposed that compresses VCG signal by optimizing the tunable quality wavelet transform (TQWT) parameters. The noise in VCG signal is firstly removed by applying a Savitzky-Golay filter, and then passing noise-free signal to an optimization algorithm that optimizes the TQWT parameters, and obtains the frequency domain signal. This signal is then quantized through dead-zone quantization and processed by a lossless compression mechanism: run-length encoding (RLE) to improve the compression ratio & encode the signal. This compressed signal is reconstructed by Inverse RLE to obtain the decoded signal. Inverse of TQWT is applied to get the reconstructed signal back from the transformed frequency domain to time domain. The parameters of TQWT, especially theQandR, are optimized to get the highestCRat lowest percent root-mean-square-difference(PRD)with best reconstruction quality and least distortions, along with acceptable values of signal-to-noise-ratio(SNR), quality score(QS), andSimilaritywith lowest mean-square-error(MSE). The comparative analysis of different optimization methods indicates that the sparse-particle swarm optimization is best among all the approaches for the tuning of parameters in TQWT for VCG signal compression and reconstruction achieving aCRof 48.18 at aPRDof 3.68,SNRof 29.39,QSof 15.71, similarity of 0.99845,MSEof 0.00016, withQvalue of 2.04307 andRvalue of 1.20568 withcomputational timeof 4.48508 s.

Electroencephalography (EEG) data comes with a large size due to the data's high sampling rate. Therefore, compressing EEG data is very important for storing the EEG files efficiently with less space and bandwidth capacity requirement. This research develops an efficient system for EEG data compression. The recorded EEG data are preprocessed and scaled using certain Resolution Factor and truncated to integer numbers, then the scaled EEG samples are classified into small and large vectors using a proposed adaptive thresholding which is based on using three computed factors: Standard deviation, Average of samples (Mean), and the multiplier factor (α). Then, each sample is passed through one of three procedures, then saved into the output file using multi-shift coding algorithm The best values are chosen as the tradeoff between the compression ratio and the processing time. The results indicated that the value of α parameter is significantly affects the threshold calculation, where the best-proven value for α is 1.30; the system achieves a compression gain of 65% while managing a reasonable processing time of 4.007 Second. The resolution factor affected the Mean Squared Error (MSE) and Mean Absolute Error (MEA) significantly, but it had a slight effect on the Compression Ratio (Cr). The α parameter has a great effect on Cr and a slight on MSE. The findings show a consistent trend whereby, as the resolution factor gradually decreases from 2 to 0.1, a concurrent decrease is observed in the MAE, MSE, Bitrate, Cr, and the overall processing time.

To address the power constraints of the emerging Internet of Things (IoT) era, we propose a compression-efficient feature extraction method for a CMOS image sensor that can extract binary feature data. This sensor outputs six-channel binary feature data, comprising three channels of binarized luminance signals and three channels of horizontal edge signals, compressed via a run length encoding (RLE) method. This approach significantly reduces data transmission volume while maintaining image recognition accuracy. The simulation results obtained using a YOLOv7-based model designed for edge GPUs demonstrate that our approach achieves a large object recognition accuracy () of 60.7% on the COCO dataset while reducing the data size by 99.2% relative to conventional 8-bit RGB color images. Furthermore, the image classification results using MobileNetV3 tailored for mobile devices on the Visual Wake Words (VWW) dataset show that our approach reduces data size by 99.0% relative to conventional 8-bit RGB color images and achieves an image classification accuracy of 89.4%. These results are superior to the conventional trade-off between recognition accuracy and data size, thereby enabling the realization of low-power image recognition systems.

This paper presents a dataset-level evaluation of six lossless compression and data transformation techniques applied to visual-cryptographic (VC) shares derived from QR codes. We processed 40,000 QR samples, comprising 10,000 QR images (Versions 1-4, 2,500 per version), 10,000 QR images (Versions 1-10, 1,000 per version across ten application domains), and 20,000 augmented QR images (with noise, rotation, shear, cropping, and brightness variations). Each QR image is converted to VC share (share1), flattened to a bitstream, and evaluated under traditional compression techniques such as Run Length Encoding (RLE), Huffman Coding, Lempel Ziv-Welch (LZW), and data transformation techniques such as Binary-to-Integer, Base64 Encoding, and (BWT + MTF + Huffman Coding) Burrows Wheeler Transform (BWT), Move-To-Front (MTF), and Huffman Coding as a combined pipeline. Our experiments report Shannon entropy, compressed character count, compressed character count percentage, compression time, decompression time, memory usage, peak memory, lossless fidelity, metadata, payload size, storage size, and compression ratio. Empirical results show near-maximal entropy in QR-derived VC data (∼0.99), providing constraints on compression performance for traditional algorithms. Base64 consistently yields the best compression performance across both clean and augmented datasets, with an average compression rate of 499%. This work contributes a reproducible pipeline, a generalized dataset, and a benchmark reference for compression research on a highly randomized binary dataset.

Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks. However, effectively integrating image segmentation into these models remains a significant challenge. In this work, we propose a novel text-as-mask paradigm that casts image segmentation as a text generation problem, eliminating the need for additional decoders and significantly simplifying the segmentation process. Our key innovation is semantic descriptors, a new textual representation of segmentation masks where each image patch is mapped to its corresponding text label. We first introduce image-wise semantic descriptors, a patch-aligned textual representation of segmentation masks that integrates naturally into the language modeling pipeline. To enhance efficiency, we introduce the Row-wise Run-Length Encoding (R-RLE), which compresses redundant text sequences, reducing the length of semantic descriptors by 74% and accelerating inference by $3\times$, without compromising performance. Building upon this, our initial framework Text4Seg achieves strong segmentation performance across a wide range of vision tasks. To further improve granularity and compactness, we propose box-wise semantic descriptors, which localizes regions of interest using bounding boxes and represents region masks via structured mask tokens called semantic bricks. This leads to our refined model, Text4Seg++, which formulates segmentation as a next-brick prediction task, combining precision, scalability, and generative efficiency. Comprehensive experiments on natural and remote sensing datasets show that Text4Seg++ consistently outperforms state-of-the-art models across diverse benchmarks without any task-specific fine-tuning, while remaining compatible with existing MLLM backbones. Our work highlights the effectiveness, scalability, and generalizability of text-driven image segmentation within the MLLM framework.

Satellite on-board registration is becoming increasingly prevalent since it shortens the data processing chain, enabling users to acquire actionable information more efficiently. However, current on-board processing hardware exhibits severely constrained storage and computational resources, making traditional ground-based methods infeasible in terms of storage and time efficiency. Meanwhile, real-time orbit parameters are normally less accurate, causing a large initial geolocation offset. In this paper, we propose a novel registration framework based on a well-designed lightweight universal database to address the challenges of limited storage as well as poor initial accuracy. Firstly, for the global matching step, a lightweight universal database is designed by storing a feature vector of control points instead of a traditional basemap (such as Digital Orthophoto Map and Digital Surface Model) for on-board processing. We replace the keypoint detection stage with a sparse sampling strategy, which significantly improves time efficiency. In addition, the sparsely sampled control points avoid the problem of keypoint repeatability, allowing the proposed method to perform robust global matching with few control points and little storage usage. Then, for the local matching step, we introduce relative total variation to extract the most obvious and significant structures from the basemap, so that unimportant feature or noise can be omitted from the database. Combined with Run-Length Encoding, the masked binary edge feature yields high precision with considerably reduced storage. Quantitative experiments demonstrate that the proposed reference database occupies less than 5% of raw image storage, while maintaining efficiency and accuracy comparable to SOTA methods.

A hybrid model for multimedia data compression using generative adversarial networks and chaotic encryption

Real-time lossless image compression based on the JPEG-LS algorithm is in high demand for critical missions such as satellite remote sensing and space exploration due to its excellent balance between complexity and compression rate. However, few researchers have made appropriate modifications to the JPEG-LS algorithm to make it more suitable for high-speed hardware implementation and application to Bayer pattern data. This paper addresses the current limitations by proposing a real-time lossless compression system specifically tailored for Bayer pattern images from spaceborne cameras. The system integrates a hybrid encoding strategy modified from JPEG-LS, combining run-length encoding, predictive encoding, and a non-encoding mode to facilitate high-speed hardware implementation. Images are processed in tiles, with each tile’s color channels processed independently to preserve individual channel characteristics. Moreover, potential error propagation is confined within a single tile. To enhance throughput, the compression algorithm operates within a 20-stage pipeline architecture. Duplication of computation units and the introduction of key-value registers and a bypass mechanism resolve structural and data dependency hazards within the pipeline. A reorder architecture prevents pipeline blocking, further optimizing system throughput. The proposed architecture is implemented on a XILINX XC7Z045-2FFG900C SoC (Xilinx, Inc., San Jose, CA, USA) and achieves a maximum throughput of up to 346.41 MPixel/s, making it the fastest architecture reported in the literature.

Since Heart Disease (HD) is a major global cause of mortality, improving patient outcomes requires both early detection and ongoing monitoring. The opportunity to implement an Internet of Things (IoT) solution has increased with the popularity of smart wearable devices. Regrettably, those who have unexpected heart attacks have a poor chance of surviving. In this paper, a Hybrid Salp Swarm Intelligence-Multi-Featured Recurrent Neural Network (HSSI-MFRNN) is proposed to enhance the ability to diagnose and track cardiac disease with greater precision and efficiency. First, patient health records are verified and uploaded using Run-Length Encoding (RLE) to compress and store the data. The Rivest-Shamir-Adleman (RSA) method is used to secure the transmission of patient data, offering a practical approach to protect sensitive medical data. The gathered data contains 70,000 records, each representing a synthetic patient healthcare record. The preprocessing step includes min-max normalization to ensure data consistency and dependability. This system uses the HSSI to optimize feature selection, identifying the most critical variables from extensive patient data and feeding these selected features into a sophisticated MFRNN architecture, which captures temporal dependencies and patterns in the data. The findings of this research show that the suggested system is beneficial in numerous areas, including data compression (DC) and secure data transfer. The comparative analysis, which has 0.921341 an accuracy, 0.934189 a precision, a (0.90135) F1-score, and 0.959532 an Area under the Curve (AUC) metric, shows that the proposed approach outperforms other traditional methods in HD diagnosis. The suggested method involves monitoring, making it a valuable tool for improving patient outcomes and reducing the financial impact of heart disease on healthcare providers.

In the context of secure and efficient data transmission over the future Internet, particularly for remote sensing and geospatial applications, reversible data hiding (RDH) in encrypted hyperspectral images (HSIs) has emerged as a critical technology. This paper proposes a novel RDH scheme specifically designed for encrypted HSIs, offering enhanced embedding capacity without compromising data security or reversibility. The approach introduces a multi-layer block labeling mechanism that leverages the similarity of most significant bits (MSBs) to accurately locate embeddable regions. To minimize auxiliary information overhead, we incorporate an Extended Run-Length Encoding (ERLE) algorithm for effective label map compression. The proposed method achieves embedding rates of up to 3.79 bits per pixel per band (bpppb), while ensuring high-fidelity reconstruction, as validated by strong PSNR metrics. Comprehensive security evaluations using NPCR, UACI, and entropy confirm the robustness of the encryption. Extensive experiments across six standard hyperspectral datasets demonstrate the superiority of our method over existing RDH techniques in terms of capacity, embedding rate, and reconstruction quality. These results underline the method’s potential for secure data embedding in next-generation Internet-based geospatial and remote sensing systems.

Let Σ be a nonempty set of characters, called an alphabet. The run-length encoding (RLE) algorithm processes any nonempty string u over Σ and produces two outputs: a k-tuple (b1,b2,…,bk), where each bi is a character and bi+1≠bi; and a corresponding k-tuple (q1,q2,…,qk) of positive integers, so that the original string can be reconstructed as u=b1q1b2q2…bkqk. The integer k is termed the run-length of u, and symbolized by ρ(u). By convention, we let ρ(ε)=0. In the Euclidean space (Rn,∥·∥2), the volume of a sphere is determined solely by the dimension n and the radius, following well-established formulas. However, for spheres of strings under the edit metric, the situation is more complex, and no general formulas have been identified. This work intended to show that the volume of the sphere SL(u,1), composed of all strings of Levenshtein distance 1 from u, is dependent on the specific structure of the “RLE-decomposition” of u. Notably, this volume equals (2l(u)+1)s−2l(u)−ρ(u), where ρ(u) represents the run-length of u and l(u) denotes its length (i.e., the number of characters in u). Given an integer p≥2, we present a partial result concerning the computation of the volume |SL(u,p)| in the specific case where the run-length ρ(u)=1. More precisely, for a fixed integer n≥1 and a character a∈Σ, we explicitly compute the volume of the Levenshtein sphere of radius p, centered at the string u=an. This case corresponds to the simplest run structure and serves as a foundational step toward understanding the general behavior of Levenshtein spheres.

Recent viral outbreaks motivate the systematic collection of pathogenic genomes in order to accelerate their study and monitor the apparition/spread of variants. Due to their limited length and temporal proximity of their sequencing, viral genomes are usually organized, and analyzed as oversized Multiple Sequence Alignments (MSAs). Such MSAs are largely ungapped, and mostly homogeneous on a column-wise level but not at a sequential level due to local variations, hindering the performances of sequential compression algorithms. In order to enable an efficient handling of MSAs, including subsequent statistical analyses, we introduce CREMSA (Column-wise Run-length Encoding for MSAs), a new index that builds on sparse bitvector representations to compress an existing or streamed MSA, all the while allowing for an expressive set of accelerated requests to query the alignment without prior decompression. Using CREMSA, a 65 GB MSA consisting of 1.9M SARS-CoV 2 genomes could be compressed into 22 MB using less than half a gigabyte of main memory, while executing access requests in the order of 100 ns. Such a speed up enables a comprehensive analysis of covariation over this very large MSA. We further assess the impact of the sequence ordering on the compressibility of MSAs and propose a resorting strategy that, despite the proven NP-hardness of an optimal sort, induces greatly increased compression ratios at a marginal computational cost. CREMSA is freely accessible at https://gitlab.univ-lille.fr/cremsa/cremsa. The Snakemake workflow for the benchmarks is available at: https://gitlab.univ-lille.fr/cremsa/bench. The data used in the paper is on Zenodo at https://zenodo.org/records/14698859 and https://zenodo.org/records/15100011.

Background: Accurate diagnosis and treatment rely on medical imaging, which presents challenges due to the vast data generated by MRIs and CT scans. Managing such volumes is complex in storage and transmission. Efficient image compression techniques are essential for telemedicine and cloud-based systems, enabling seamless data transfer while preserving quality. Materials and Methods: This study compares three widely used compression techniques: Adaptive Huffman Coding (lossless), Discrete Cosine Transform (DCT) (lossy), and Adaptive Multi-Layer Run-Length Encoding (AMLRLE) (lossless). A dataset of DICOM medical images was used, and techniques were evaluated based on three key performance metrics: compression ratio (CR) for data reduction, processing time (PT) for computational efficiency, and Peak Signal-to-Noise Ratio (PSNR) for assessing image quality. Results: Huffman Coding, a lossless technique, achieved a high compression ratio of 0.972 with an average compression time of 0.028 seconds. However, it exhibited lower image quality than DCT and AMLRLE. DCT, a lossy method that converts image data into frequency components, provided a compression ratio of 0.964, a processing time of 0.088 seconds, and a PSNR of 317.55 dB. AMLRLE, another lossless technique, showed performance nearly identical to DCT, maintaining the same compression ratio, processing time, and PSNR. Conclusion: Huffman Coding suits applications needing fast processing, while DCT and AMLRLE are better for high-quality imaging. The choice of compression method depends on system needs—speed, storage, or diagnostic precision. Future research will integrate these techniques with machine learning to enhance adaptive compression for medical imaging.

This paper introduces a new memory-efficient algorithm for connected-components labeling in binary images, which is based on run-length encoding. Unlike conventional pixel-based methods that scan and label individual pixels using global buffers or disjoint-set structures, our approach encodes rows as linked segments and merges them using a union-by-size strategy. We accelerate run detection by using a precomputed 16-bit cache of binary patterns, allowing for fast decoding without relying on bitwise CPU instructions. When compared against other run-length encoded algorithms, such as the Scan-Based Labeling Algorithm or Run-Based Two-Scan, our method achieves up to 35% faster on most real-world datasets. While other binary-optimized algorithms, such as Bit-Run Two-Scan and Bit-Merge Run Scan, are up to 45% faster than our algorithm, they require much higher memory usage. Compared to them, our method tends to reduce memory consumption on some large document datasets by up to 80%.

With rapid advancements in medical imaging tech-nology, a substantial amount of image data has been produced to assist clinical diagnostics. Nevertheless, storing and trans-mitting medical images with high-resolution content presents a formidable challenge that needs to be addressed. This study pro-poses a technique to compress DICOM images using a Modified variant of Discrete Wavelet Transform (MDWT) including Run-Length Encoding and DEFLATE algorithm. The proposed mech-anism decomposes a DICOM image into its frequency sub-bands, namely, approximation (LL), horizontal detail (LH), vertical detail (HL), and diagonal detail (HH) coefficients which are then thresholded and quantized in an adaptive manner using uniform scalar quantization. The quantized coefficients are run-length encoded with a modified scheme to traverse the data including linear, diagonal, and spiral approaches. Subsequently, DEFLATE algorithm-based compression is performed for further reduction in data volume. Results indicate a noteworthy improvement in compression ratio with the modifications while preserving a high level of detail.

The rising popularity of digital image sharing requires verification systems for visual content authenticity.Digital media reliability suffers because of image tampering that takes place on social platforms.The current detection approaches fail with degraded images while unable to restore lost content.This research presents PFDNet as a deep learning-based framework which detects photo tampering and restores authentic content through its framework.The Cyber Vaccinator module generates a tamper-proof updated image through integration of the actual content with edge information.The Invertible Neural Network (INN) performs alteration detection in its forward process and restores original content in its backward process.The accuracy verification function of Run-Length Encoding (RLE) exists for restoration purpose.The experiment results demonstrate PFDNet successfully recognizes tampered images while restoring them faithfully and maintaining their authenticity.

Image compression plays a crucial role in optimising storage and transmission efficiency. This paper evaluates the performance of Run-Length Encoding (RLE), Huffman Coding, and Lempel-Ziv-Welch (LZW) algorithms for compressing grayscale PNG and JPG images. The study analyses their effectiveness using compression ratio, bits per pixel, and compression time as key performance metrics. Results indicate that LZW achieved the highest compression ratio, ranging from 1.0113 to 2.4020, making it the most efficient for file size reduction. RLE performed moderately, with compression ratios between 0.5456 and 2.3895, while Huffman Coding exhibited the lowest ratios, ranging from 0.2646 to 1.0680. In terms of bits per pixel, LZW recorded the lowest values, highlighting its ability to reduce data while preserving image quality. Compression time analysis revealed that RLE was the fastest, with processing times between 0.0019 and 0.0468 seconds, making it suitable for real-time applications. LZW and Huffman Coding demonstrated a trade-off between compression efficiency and speed. These findings establish LZW as the most effective algorithm for high compression with minimal quality loss, while RLE remains the best option for speed-critical applications.

This paper introduced a novel storage format for covering arrays, designed to optimize file size through efficient compression techniques. The proposed format employed Asymmetric Numeral System (ANS) encoding for array data, as well as Run-Length Encoding (RLE) and Variable Length Encoding (VLE) for metadata storage. The goal was to provide a compact, standardized format that facilitates easier sharing and utilization of covering arrays across different applications. Experimental evaluations on a dataset of 21964 covering arrays from the National Institute of Standards and Technology (NIST) demonstrated that the new format outperforms general-purpose compression algorithms such as ZIP, BZIP2, and XZ in most cases, particularly for larger covering arrays with high parameter counts. While previous work on covering array storage focused on archival and retrieval efficiency, the proposed method significantly reduces storage requirements without loss of structural integrity. The proposed method preserved the combinatorial properties of covering arrays while reducing redundancy, making it a practical alternative for large-scale combinatorial testing applications.

ABSTRACTContinuous photoacoustic microscopy (PAM) imaging generates large volumes of data, resulting in significant storage demands. Here, we propose a high‐fidelity real‐time compression algorithm for PAM data in LabVIEW by combining Discrete Cosine Transform (DCT) with adaptive thresholding and Run Length Encoding (RLE), which we term Adaptive Run Length Encoded DCT (AR‐DCT) compression. This algorithm reduces data storage requirements while preserving all the details of the images. AR‐DCT ensures real‐time compression, achieving superior compression ratios (CRs) compared to traditional DCT compression. We evaluated the performance of AR‐DCT using in vivo mouse brain imaging data, demonstrating a CR of ~50, with a structural similarity index of 0.980 and minimal degradation in signal quality (percentage‐root‐mean‐square‐difference of 1.345%). The results show that AR‐DCT outperforms traditional DCT, offering higher compression efficiency without significantly sacrificing image quality. These findings suggest that AR‐DCT provides an effective solution for applications requiring continuous experiments, such as cerebral hemodynamics studies.

Due to the maximal transistor count, Multi-Processor System-on-Chip (MPSoC) delivers more performance than uniprocessor systems. Network on Chip (NoC) in MPSoC provides scalable connectivity compared to traditional bus-based interconnects. Still, NoC designs significantly impact MPSoC design as it increases power consumption and network latency. A solution to this problem is packet compression which minimizes the data redundancy within NoC packets and reduces the overall power consumption of the whole network by minimizing a data packet size. Latency and overhead of compressor and decompressor require more memory access time, even though the packet compression is good for the improved performance of NoC. So, this problem demands a simple and lightweight compression method like delta compression. Consequently, this research proposes a new delta-difference Hybrid Tree coding (∆DHT-Zip) to de/compress the data packet in the NoC framework. In this compression approach, the Delta encoding, Huffman encoding and DNA tree (deoxyribonucleic acid) coding are hybridized to perform the data packet de/compression approach. Moreover, a time series approach named Run Length Encoding (RLE) is used to compress the metadata obtained from both the encoding and decoding processes. This research produces decreased packet loss and significant power savings by using the proposed ∆DHT-Zip method. The simulation results show that the proposed ∆DHT-Zip algorithm minimizes packet latency and outperforms existing data compression approaches with a mean Compression Ratio (CR) of 1.2%, which is 79.06% greater than the existing Flitzip algorithm.