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Holotomography (HT) is a label-free, three-dimensional quantitative phase imaging technique that captures refractive index distributions of biological samples at sub-micron resolution. As modern HT systems enable high-throughput and large-scale acquisition, they produce terabyte-scale datasets that require efficient data management. This study presents a systematic benchmarking of data compression strategies for HT data stored in the OME-Zarr format, a cloud-compatible chunked data structure suitable for scalable imaging workflows. Using six representative datasets from five biological samples, we evaluated combinations of preprocessing filters and 13 compression algorithms across multiple compression levels. Performance was assessed in terms of compression ratio, bandwidth, and decompression speed. A throughput-based evaluation metric was introduced to capture realistic performance under varying network constraints, revealing that the optimal compression strategy is strongly dependent on available system bandwidth. Across a wide range of bandwidth conditions, Pcodec consistently exhibited the most balanced overall performance, followed by Blosc-zstd and zstd. The results offer practical guidance for the storage and transmission of large HT datasets and serve as a reference for implementing scalable, FAIR-aligned imaging workflows in cloud and high-performance computing environments.

Single-pixel holography (SPH) is capable of generating holographic images with rich spatial information by employing only a single-pixel detector. Thanks to the relatively low dark-noise production, high sensitivity, large bandwidth, and cheap price of single-pixel detectors in comparison to pixel-array detectors, SPH is becoming an attractive imaging modality at wavelengths where pixel-array detectors are not available or prohibitively expensive. In this work, we develop a high-throughput single-pixel compressive holography with a space-bandwidth- time product (SBP-T) of 41,667 pixels/s, realized by enabling phase stepping naturally in time and abandoning the need for phase-encoded illumination. This holographic system is scalable to provide either a large field of view (~83 mm 2 ) or a high resolution (5.80 μm × 4.31 μm). In particular, high-resolution holographic images of biological tissues are presented, exhibiting rich contrast in both amplitude and phase. This work is an important step towards multi-spectrum imaging using a single-pixel detector in biophotonics. Single-pixel holography generates holographic images with a single-pixel detector making this relatively inexpensive. Here the authors report a high-throughput single-pixel compressive holography method for imaging biological tissue which can either provide a large field of view or high resolution.

The rapid development of digital media demands higher quality in pattern design. Simultaneously, significant redundant information hinders the transmission and sharing of these patterns, creating an urgent need for image compression. However, traditional image compression methods struggle to balance efficiency and quality. To address this, a new image compression model for digital media patterns is proposed, based on K-means clustering and Locally Linear Embedding methods. This model creates an efficient compression solution by integrating dynamic clustering parameter selection based on color histograms, multi-dimensional image segmentation of color and texture, and a local linear embedding dimensionality reduction algorithm using dynamic neighborhood selection. The model achieves a compression ratio of 84% and a Peak Signal-to-Noise Ratio of 41dB, with no significant quality difference before and after compression, validating the effectiveness of the improvements made to the basic methods. In practical application experiments, the model's multi-scale structural similarity reaches 0.71. When processing a large number of patterns, the fastest response time is 189ms, with a minimum memory usage of 16.1M. The shortest processing time on a single-core processor is 0.34s. These experimental results demonstrate that the model balances compression efficiency and quality, offering superior compression performance, good robustness, and the ability to handle various complex tasks and adapt to different application scenarios, meeting the high standards of the digital media industry for image compression.

Secure image transfer is a critical requirement in telemedicine and Picture Archiving and Communication Systems (PACS), where diagnostic integrity and patient confidentiality must be simultaneously ensured. High-resolution medical images, especially Magnetic Resonance Imaging (MRI), are often transferred over bandwidth-limited networks without impairing diagnostic quality or confidentiality against unauthorized access, manipulation, and replay. Existing approaches generally rely on a cascaded pipeline where image compression, encryption, and verification are considered as separate, independent operations. These distributed designs are prone to cryptographic key management issues, suffer from non-deterministic error control, lack meaningful coupling between image identity and cryptographic state, and provide no verifiable provenance or post-quantum key migration support. In this paper, we propose a DICOM-compliant system that integrates ϵ -regularized near-lossless image compression, hyperchaotic permutation diffusion encryption, post-quantum secure key establishment, and blockchain-based provenance verification as a unified operation. JPEG 2000 is used for primary image compression, and a residual refinement layer is introduced to limit residual correction amplitudes by ϵ -regularization to retain diagnostically significant information. Hyperchaotic encryption is performed with a symmetric image key generated using the NIST-standard post-quantum Key Encapsulation Mechanism (ML-KEM) and extracted using the HMAC-based Key Derivation Function (HKDF), cryptographically linked to DICOM image metadata. Message integrity is ensured using a keyed-hash message authentication code (HMAC), while a permissioned blockchain records only non-sensitive cryptographic metadata to enable verifiable provenance. Experimental results on clinical MRI and susceptibility-weighted imaging datasets demonstrate high reconstruction quality (PSNR: 46–51 dB, SSIM: 0.92–0.97), compression ratios ranging from 7.14:1 to 23.03:1 depending on modality and ϵ , and strong cryptographic properties (NPCR: 99.6%, UACI: 33.46%, entropy: 7.9993). The evaluation focuses on MRI-based modalities, reflecting the intended scope of this study.

Image compression has made significant progress through end-to-end deep-learning approaches in recent years. The Transformer network, coupled with self-attention mechanisms, efficiently captures high-frequency features during image compression. However, the low-frequency information in the image cannot be obtained well through the Transformer network. To address this issue, the paper introduces a novel end-to-end autoencoder architecture for image compression based on the transformer and residual network. This method, called Transformer and Residual Network (TRN), offers a comprehensive solution for efficient image compression, capturing essential image content while effectively reducing data size. The TRN employs a dual network, comprising a self-attention pathway and a residual network, intricately designed as a high-low-frequency mixer. This dual-network can preserve both high and low-frequency features during image compression. The end-to-end training of this model employs rate-distortion optimization (RDO methods). Experimental results demonstrate that the proposed TRN method outperforms the latest deep learning-based image compression methods, achieving an impressive 8.32% BD-rate (bit-rate distortion performance) improvement on the CLIC dataset. In comparison to traditional methods like JPEG, the proposed achieves a remarkable BD-rate improvement of 70.35% on the CLIC dataset.

The explosive growth of medical data poses significant challenges for storage and sharing. Current compression techniques utilizing Implicit Neural Representations (INRs) effectively strike a balance between encoding accuracy and compression ratio, yet they suffer from slow encoding speeds. By contrast, data-driven compressors encode fast but heavily rely on the training data and cannot generalize well. To develop a practical compression tool overcoming all these limitations, we introduce Shape-Texture Decoupled Compression (DeepSTD), which focuses on the data set of the same modality and body parts and proposes decoupling the variations into shape and texture components for separate encoding. Disentangling two components facilitates designing proper encoding strategies suitable for their respective characteristics—swift shape encoding based on INRs and effective data-driven texture encoding. The proposed approach combines the advantages of INR-based and data-driven models, to achieve high fidelity, fast encoding speed, as well as good generalizability. Comprehensive evaluations on large-scale Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) datasets demonstrate superior performance across encoding quality, compression ratio, and speed. Besides, with features like parallel acceleration with multiple Graphics Processing Units (multi-GPU), flexible control of compression ratio, and broad applicability, DeepSTD offers a robust and efficient solution for the pressing demands of modern medical data compression. The authors introduce Shape-Texture Decoupled Compression (DeepSTD), a practical compression tool for medical imaging data. They provide evaluations on different modality imaging datasets, providing a route towards a solution for the pressing demands of modern medical data compression.

Background High-throughput sequencing technologies generate massive amounts of FASTQ data comprising nucleotide sequences, quality scores, and read identifiers, necessitating efficient compression to alleviate storage and transmission burdens. Compared to general-purpose compressors, specialized FASTQ compressors achieve higher compression performance by exploiting the inherent redundancy in FASTQ files. However, existing FASTQ-specialized compressors often suffer from limited data applicability and tend to over-optimize either compression ratio or compression speed at the expense of the other. Results We present zDUR, a reference-free FASTQ compressor designed for efficient and scalable handling of next-generation sequencing data across diverse platforms and sequencing data types. Benchmarking against six reference-free compressors on 15 representative datasets spanning four sequencing data types demonstrates that zDUR achieves a favorable overall balance between compression ratio and speed, with broad applicability across data types. In particular, on single-cell RNA-seq and spatial transcriptomics datasets, zDUR achieves over a tenfold increase in runtime performance while maintaining higher compression ratios than SPRING, one of the state-of-the-art reference-free FASTQ compressors. Conclusions zDUR offers a scalable and efficient solution for reference-free FASTQ compression, balancing performance, speed, and usability across diverse datasets.

Acceleration of MR sequences beyond current parallel imaging techniques is possible with the Compressed SENSE technique that has recently become available for 1.5 and 3 Tesla scanners, for nearly all image contrasts and for 2D and 3D sequences. The impact of this technique on examination timing parameters and MR protocols in a clinical setting was investigated in this retrospective study. A numerical analysis of the examination timing parameters (scan time, exam time, procedure time, interscan delay time, changeover time, nonscan time) based on the MR protocols of 6 different body regions (brain, knee, lumbar spine, breast, shoulder) using MR log files was performed and the total number of examinations acquired from January to April both in 2017 and 2018 on a 1.5 T MR scanner was registered. Percentages, box plots and unpaired two-sided t tests were obtained for statistical evaluation. All examination timing parameters of the six anatomical regions analysed were significantly shortened after implementation of Compressed SENSE. On average, scan times were accelerated by 20.2% (p<0.0001) while procedure times were shortened by 16% (p<0.0001). Considering all anatomical regions and all MR protocols, 27% more examinations were performed over the same 4 month period in 2018 compared to 2017. Compressed SENSE allows for a significant acceleration of MR examinations and a considerable increase in the total number of MR examinations is possible.

Centrifuger is an efficient taxonomic classification method that compares sequencing reads against a microbial genome database. In Centrifuger, the Burrows-Wheeler transformed genome sequences are losslessly compressed using a novel scheme called run-block compression. Run-block compression achieves sublinear space complexity and is effective at compressing diverse microbial databases like RefSeq while supporting fast rank queries. Combining this compression method with other strategies for compacting the Ferragina-Manzini (FM) index, Centrifuger reduces the memory footprint by half compared to other FM-index-based approaches. Furthermore, the lossless compression and the unconstrained match length help Centrifuger achieve greater accuracy than competing methods at lower taxonomic levels.

In remote sensing applications and medical imaging, one of the key points is the acquisition, real-time preprocessing and storage of information. Due to the large amount of information present in the form of images or videos, compression of these data is necessary. Compressed sensing is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, by using a minimum number of nonadaptive linear measurements. After this compressed sensing process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose, in this work, a compressed sensing reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by using an augmented Lagrangian that avoids the difficult problem of nonlinearity and nondifferentiability of the regularization terms. The proposed algorithm, called denoising-compressed sensing by regularization (DCSR) terms, will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov’s algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach permits a gain up to 25% in terms of denoising efficiency and visual quality using two metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).