Simple, Fast, and Efficient Natural Language Adaptive Compression

High resolution digital medical images are stored in DICOM (Digital Imaging and Communications in Medicine) format that requires high storage space in database. Therefore reducing the image size while maintaining diagnostic quality can increase the memory usage efficiency in PACS. In this study, diagnostic regions of interest (ROI) of pelvis radiographs marked by the radiologist are segmented and adaptively compressed by using image processing algorithms. There are three ROIs marked by red, blue and green in every image. ROI contoured by red is defined as the most significant region in the image and compressed by lossless JPEG algorithm. Blue and green regions have less importance than the red region but still contain diagnostic data compared to the rest of the image. Therefore, these regions are compressed by lossy JPEG algorithm with higher quality factor than rest of the image. Non-contoured region is compressed by low quality factor which does not have any diagnostic information about the patient. Several compression ratios are used to determine sufficient quality and appropriate compression level. Compression ratio (CR), peak signal to noise ratio (PSNR), bits per pixel (BPP) and signal to noise ratio (SNR) values are calculated for objective evaluation of image quality. Experimental results show that original images can approximately be compressed six times without losing any diagnostic data. In pelvis radiographs marking multiple regions of interest and adaptive compression of more than one ROI is a new approach. It is believed that this method will improve database management efficiency of PACS while preserving diagnostic image content.

Cache-block compression is a highly effective technique for both reducing accesses to lower levels in the memory hierarchy (cache compression) and minimizing data transfers (link compression). While many effective cache-block compression algorithms have been proposed, the design of these algorithms is largely ad hoc and manual and relies on human recognition of patterns. In this article, we take an entirely different approach. We introduce a class of “byte-select” compression algorithms, as well as an automated methodology for generating compression algorithms in this class. We argue that, based on upper bounds within the class, the study of this class of byte-select algorithms has potential to yield algorithms with better performance than existing cache-block compression algorithms. The upper bound we establish on the compression ratio is 2X that of any existing algorithm. We then offer a generalized representation of a subset of byte-select compression algorithms and search through the resulting space guided by a set of training data traces. Using this automated process, we find efficient and effective algorithms for various hardware applications. We find that the resulting algorithms exploit novel patterns that can inform future algorithm designs. The generated byte-select algorithms are evaluated against a separate set of traces and evaluations show that Byte-Select has a 23% higher compression ratio on average. While no previous algorithm performs best for all our data sets which include CPU and GPU applications, our generated algorithms do. Using an automated hardware generator for these algorithms, we show that their decompression and compression latency is one and two cycles respectively, much lower than any existing algorithm with a competitive compression ratio.

Pixelwise linear prediction using backward-adaptive least-squares or weighted least-squares estimation of prediction coefficients is currently among the state-of-the-art methods for lossless image compression. While current research is focused on mean intensity prediction of the pixel to be transmitted, best compression requires occurrence probability estimates for all possible intensity values. Apart from common heuristic approaches, we show how prediction error variance estimates can be derived from the (weighted) least-squares training region and how a complete probability distribution can be built based on an autoregressive image model. The analysis of image stationarity properties further allows deriving a novel formula for weight computation in weighted least-squares proofing and generalizing ad hoc equations from the literature. For sparse intensity distributions in non-natural images, a modified image model is presented. Evaluations were done in the newly developed C++ framework volumetric, artificial, and natural image lossless coder (Vanilc), which can compress a wide range of images, including 16-bit medical 3D volumes or multichannel data. A comparison with several of the best available lossless image codecs proofs that the method can achieve very competitive compression ratios. In terms of reproducible research, the source code of Vanilc has been made public.

Time series compression encodes the information in a time-ordered sequence of data points into fewer bits, thereby reducing storage costs and possibly other costs. Compression methods are either general or XOR-based. General compression methods are time-consuming and are not suitable in streaming scenarios, while XOR-based methods are unable to consistently maintain high compression ratios. Further, existing methods compress the integer and decimal parts of floating-point values as a whole, thus disregarding the different characteristics of the two parts. We propose Camel , a new compression method for floating-point time series with the goal of advancing the compression ratios and efficiency achievable. Camel compresses the integer and decimal parts of the double-precision floating-point numbers in time series separately; and instead of performing XOR operations on values using their previous value, Camel identifies values that enable higher compression ratios. Camel also includes means of indexing compressed data, thereby making it possible to query compressed data efficiently. We report on an empirical study of Camel and 11 lossless and 6 lossy compression methods on 22 public datasets and three industrial datasets from AliCloud. The study offers evidence that Camel is capable of outperforming existing methods in terms of both compression ratio and efficiency and is capable of excellent compression performance on both time series and non-time series data.

Reliable transmission of high quality images through wireless communication channels represents a great challenge, especially for real-time applications. From one aspect, it is necessary to achieve efficient compression that maintains the good image quality without increasing the transmission bandwidth. Secondly, wireless channels impairments such as fading and interference increase the probability of errors when the image data is transmitted. This necessitates the use of efficient channel coding to protect against channel errors. However channel coding results in increasing the transmitted bit rate, for real time applications. Again, this is constrained by the usually limited available transmission bandwidth. In this paper, this dilemma is solved by adopting new unequal error protection techniques that are used to achieve the above requirements by using image adaptive combined compression and error protection. This is achieved through image segmentation and adaptive compression and channel coding according to the importance of the image segments. Two proposed techniques are introduced. In the first suggested technique, the image is partitioned into equal blocks and the data classification is performed in the DCT domain. The DCT coefficients of the different blocks are classified to different classes of importance and hence they are given different rates of error protection and compression ratios. The second technique is based on adaptive image segmentation using quadtree decomposition into unequal segments before applying the DCT. Finally, to improve the quality of received images, the Orientation Adaptive Sequential Interpolation error-concealment technique is applied to the reconstructed images. Computer simulation shows that the proposed techniques achieve high degree of robustness against channel impairments with good image quality with acceptable data rates.

SEML: A Simplified English Modeling Language for Constructing Biological Models in Julia

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes.

An efficient texture compression method is proposed based on a block matching process between the current block and the previously encoded blocks. Texture mapping is widely used to improve the quality of rendering results in real-time applications. For fast texture mapping, it is important to find an optimal trade-off between compression efficiency and computational complexity. Low-complexity methods (e.g., ETC1 and DXT1) have often been adopted in real-time rendering applications because conventional compression methods (e.g., JPEG) achieve a high compression ratio at the cost of high complexity. We propose a block matching-based compression method that can achieve a higher compression ratio than ETC1 and DXT1 while maintaining computational complexity lower than that of JPEG. Through a comparison between the proposed method and existing compression methods, we confirm our expectations on the performance of the proposed method.

In this paper, we propose a compression method that allows for the efficient storage of large amounts of CAN traffic data, which is needed for the forensic investigations of accidents caused by the cyber-attacks on vehicles. Compression of recorded CAN traffic also reduces the time (or bandwidth) needed to off-load that data from the vehicle. In addition, our compression method allows analysts to perform log analysis on the compressed data. It is shown that the proposed compression format is a powerful tool to find traces of a cyber-attack. We achieve this by performing semantic compression on the CAN traffic logs, rather than the simple syntactic compression. Our compression method is lossless, thus preserving all information for later analysis. Besides all the above advantages, the compression ratio that we achieve is better than the compression ratio of the state-of-the-art syntactic compression methods, such as zip.

We introduce a noninterferometric single beam method for accurate characterization and adaptive compression of amplified femtosecond pulses. This new method, air-MIIPS, exploits the third harmonic generation in air to measure and compensate high-order phase distortions.

Previous work on testing NoC-based systems has shown that the integrated network can be efficiently reused as test access mechanism (TAM). These approaches exploit the given infrastructure for data transportation. Beyond that, NoC-architectures for nanoscale systems also have to ensure a reliable communication by additional hardware and/or software measures. For example a cyclic redundancy check (CRC) may be added to protect the end-to-end communication between network resources. This paper presents an approach to reuse the CRC hardware for a compression scheme similar to LFSR-reseeding. The experimental results show that competitive compression ratios can be achieved at low extra cost. This way, core testing in NoC-based systems can benefit from test data compression even for standard cores without integrated decompressors.

Healthcare institutions face a growing demand to use machine learning to improve patient outcomes while adhering to strict privacy laws and data security measures. Although data heterogeneity, institutional privacy restrictions, and different contribution values across participating institutions pose challenges for current methodologies, cross-silo federated learning offers a viable alternative for collaborative healthcare analytics. In this paper, a novel paradigm for privacy-preserving healthcare collaboration in crosssilo environments is presented: adaptive contribution-based federated learning (ACFL). By modifying both compression rates and differential privacy measures according to each participant's contribution to the global model, resource availability, and data characteristics, ACFL overcomes the drawbacks of conventional federated learning. The framework introduces several important innovations: A feature importance-based differential privacy approach that applies different noise levels to model parameters based on their clinical significance, a comprehensive contribution tracking mechanism that uses exponential moving average smoothing to evaluate both parameter update magnitude and performance impact, and adaptive sparsification that maximizes communication efficiency while maintaining valuable updates. Through an experimental evaluation on the MIMIC-IV prescription dataset, adaptive compression and privacy methods of ACFL maintain to treat important characteristics while lowering communication overhead.

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

In technological developments, it also affects the size of the data from a file, especially in transmitting JPEG images or images. Digital images have a size or capacity that is large enough so that the image transmission process often experiences problems due to its large enough capacity. Large data sizes also require large storage space, therefore additional solutions are needed to save space on storage media, such as reducing the size of the data to be stored. Knowing the value of the speed and efficiency of the JPEG image compression process needs to remember so many compression methods that exist today, one of which is the FELICS method. Image compression is carried out on images aimed at reducing the redundancy of existing data in the image so that it can be stored or transmitted efficiently. The FELICS method, which stands for Fast Compression & Lossless Image System, is a lossless image compression algorithm that performs 5 times faster than the original lossless JPEG codec and produces a compression ratio of the same value. So through this research the writer wants to carry out the analysis process of the FELICS method. The Fast Compression & Lossless Image System method works very well on images and produces high ratio values. Keywords : Analysis, Image, Compression, FELICS Method

This paper proposed the Agentic Computer Vision (AgCV) framework designed to automate complex computer vision (CV) tasks through autonomous agents that communicate through Graphical User Interface (GUI). The AgCV framework leverages LangGraph, natural language processing, deep learning, and data science to build adaptive, user-driven CV pipelines. In the proposed AgCV each Agent works on a particular task ranging from object identification and classification to image segmentation. By incorporating Retrieval-Augmented Generation (RAG) and LangGraph, the AgCV enable fully automated pipelining through user interactions. The proposed Framework strategy reduces the need for technical expertise, allowing end-users to generate and configure CV operations using intuitive language commands. AgCV promotes accessibility, scalability, and flexibility of CV applications in different domains. The AgCV not only simplifies user interaction but also ensures that the system aligns with user expectations and needs.•The proposed system allows users to create and configure CV operations using simple natural language, making it accessible even to those with limited technical expertise.•The AgCV framework supports a wide range of CV tasks and can be easily adapted to different user needs and applications.