Lossless Image Compression Research Articles

Comparison of deep learning-based models for detection of diseased trees using an image compression algorithm

The object of the research is the application of deep learning algorithms using an improved mathematical lossless image compression method for recognizing and identifying dead trees in aerospace images. The main problem that has been solved is the archiving of images due to their large volume on disk and the possibility of their further processing by deep learning methods such as convolutional and capsule neural networks, which have shown high efficiency and accuracy in image recognition and classification tasks using the proposed new image compression method. The article presents a comparative analysis of the performance of three YOLO (You Only Look Once) models with different types of architectures, such as YOLOv5, YOLOv7 and YOLOv8, to assess the effectiveness of their work for the task of recognizing aerospace tree images obtained from satellites, drones, and aircrafts. Comprehensive analysis of YOLO models presents that model YOLO v8 turned out to be most effective with a positive accuracy of 88.2 %, a recall of 77.4 %, and a mAP50 score of 87.2 %. Moreover, the average detection time was only 0.052 seconds for each image, even though the model size remains very small – 21.5 MB. These results suggest a much better usage of time and precise identification of dead trees, and classified targets with high efficiency. From the research, there is significant prospects of global forest management especially on forest reduction and protection of ecosystems through accurate assessment on the health of forestry. The proposed approach is universal and can be used in real life conditions, providing a good compromise of the speed, accuracy and resources required for forest monitoring and management

Security of Magnetic Resonance Medical Images using Region-based Lossless Image Compression in Healthcare Information Systems

Purpose The purpose of region-based medical image compression is to optimize the compression process by focusing on specific regions of interest within medical images. Unlike traditional compression methods that treat the entire image uniformly, region-based compression techniques identify and prioritize certain areas or regions within the image that are deemed more diagnostically significant or relevant. By allocating more resources to compressing these critical regions while reducing compression in less important areas, region-based compression methods aim to achieve higher compression efficiency while preserving diagnostic quality. This approach is particularly valuable in medical imaging, where accurate representation of anatomical structures or pathological findings is paramount for clinical diagnosis and decision-making. Region-based compression can help reduce storage requirements, transmission bandwidth, and processing time without compromising the diagnostic integrity of medical images, thereby facilitating more efficient healthcare delivery and telemedicine applications. Methods In this study, we utilized distortion-limiting compression techniques to optimize the compression process for specific regions within medical images. We employed lossless scalable RBC (Region-Based Compression) using Discrete Wavelet Transform (DWT) for Digital Imaging and Communication in Medicine (DICOM) images. The initial step involved medical image pre-processing, followed by segmentation to separate the image into regions of interest (ROI) and non-ROI. Compression techniques were then applied to reduce network bandwidth and storage requirements. Fractal lossy compression was employed for the non-ROI portion, while context-tree weighting lossless compression was proposed for the ROI portion, effectively compressing the image while rejecting noisy background elements. During decompression, the original medical image can be reconstructed using the reverse process. This approach optimizes storage and transmission efficiency while preserving diagnostic integrity in medical imaging applications. Results The experiment involved testing various medical images, and the proposed method outperformed previous techniques in terms of results. According to the findings, the improvement in Peak Signal-to-Noise Ratio (PSNR) over current techniques reached up to 24.23 dB compared to the Joint Photographic Experts Group (JPEG). Additionally, it achieved up to 12.22 dB improvement compared to other transform approaches. These significant enhancements prompted the development of a web and mobile platform for compressing and sending medical images, particularly microscopic ones, in real time. Conclusion This research focuses on employing wavelet transform techniques to compress the Region of Interest (ROI) within medical images. This ROI-based compression approach is particularly valuable as it retains essential diagnostic information while reducing the overall file size. Such a technique holds significant promise for telemedicine systems, especially in rural regions where network resources may be limited or constrained. By selectively compressing the most diagnostically relevant areas of medical images, this approach ensures that critical information is preserved while optimizing data transmission and storage efficiency. This can ultimately enhance access to medical imaging services and facilitate remote diagnosis and treatment in underserved areas with limited network infrastructure.

Parallel Lossless Compression of Raw Bayer Images on FPGA-Based High-Speed Camera

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb–Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on AMD-Xilinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high-resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping the total memory requirements low due to real-time processing.

Effects and optimizations of image charge technology on the imaging performance of capacitive division image readout MCP detectors

A finite element model is proposed to investigate the impact of image charge technology on the imaging performance of capacitive division image readout (C-DIR) devices. Through detailed simulation analysis, we explore the charge density of the induction layer and each electrode layer in the multilayer capacitive anode. The position nonlinearity is calculated for various resistances per square of the induction layer and substrate thicknesses. The simulation results indicate a strong linear correlation between the position nonlinearity and substrate thickness, and an exponential decay relationship with the logarithmic resistance per square of the induction layer. The C-DIR detector is experimentally tested for imaging nonlinearity and counting rate, with the resistance per square of the induction layer ranging from 0.005 to 1000 MΩ, and the substrate thickness ranging from 1 to 5 mm. The experimental results validate the simulation conclusions and reveal the impact of the “charge accumulation effect” and “shielding effect” on the imaging performance of the detector, as well as the “imaging compression” phenomenon. Optimized image charge readout technology enables the C-DIR detector to achieve an imaging nonlinearity of 1.53% in the experiment.

Efficiency Evaluation of Huffman, Lempel-Ziv, And Run-Length Algorithms in Lossless Image Compression for Optimizing Storage and Transmission Efficiency

Efficiency Evaluation of Huffman, Lempel-Ziv, And Run-Length Algorithms in Lossless Image Compression for Optimizing Storage and Transmission Efficiency

Fast lossless images compression for synchrotron radiation facility using deep learning and hybrid architecture

Fast lossless images compression for synchrotron radiation facility using deep learning and hybrid architecture

Research and Implementation of FPGA Image Compression Parallel Algorithm Based on CCSDS

High-pixel, high-resolution spectral images are an important source of key graphical information obtained from space remote sensing, however, with the dramatically expanding amount of graphical information, the limited transmission bandwidth and hardware resources are difficult to meet the demand for massive data, which makes real-time image lossless compression and transmission technology an important solution. For this reason, this paper is based on the CCSDS image lossless compression algorithm, using FPGA for the construction of image compression system, and proposes a method strategy based on parallel acceleration algorithm to realize the real time processing of large-capacity hyperspectral images. The simulation results show that hyperspectral river and mountain range images can be compressed with greatly improved compression efficiency while maintaining real time performance and accuracy.

CC-SMC: Chain coding-based segmentation map lossless compression

A segmentation map, either static or dynamic, refers to a two-dimensional picture that may vary with time and indicates the segmentation label per pixel. Both the semantic map and the occupancy map in video-based point cloud compression (V-PCC) belong to the segmentation map we referred to. The semantic map can work for many machine vision tasks like tracking and has been used as a layer of image representation in some image compression methods. The occupancy map constitutes a part of the point cloud coding bitstream. Since segmentation maps are widely used, how to efficiently compress them is of interest. We propose a segmentation map lossless compression scheme namely CC-SMC, exploiting the nature of segmentation maps that usually contain limited colors and sharp edges. Specifically, we design a chain coding-based scheme combined with quadtree-based block partitioning. For intraframe coding, one block is partitioned recursively with a quadtree structure, until the block contains only one color, is smaller than a threshold, or satisfies the defined chain coding condition. We revise the three-orthogonal chain coding method to incorporate contextual information and design effective intraframe prediction methods. For interframe coding, one block may find a reference block; the chain difference between the current and the reference blocks is coded. We implement the proposed scheme and test it on several different kinds of segmentation maps. Compared with advanced lossless image compression techniques, our proposed scheme obtains more than 10% bits reduction as well as more than 20% decoding time-saving. The code is available at https://github.com/Yang-Runyu/CC-SMC.

Bayer-filtered image compression based on CCSDS-123 standard: A tailored approach for CubeSats

CubeSats, popular for enabling affordable-access to various space missions, face challenges related to their limited resources. Image compression combined with Bayer filtering, could be a potential key to maximize the efficiency of CubeSats’ limited resources. This paper aims to customize the CCSDS-123 recommendation for lossless compression of Bayer-filtered images onboard CubeSats, with emphasis on simplicity and efficiency. The results obtained with images obtained from the Alsat-1 N CubeSat demonstrate the feasibility and satisfactory performance of the proposed approach.

AstroDLLC: Efficiently Reducing Storage and Transmission Costs for Massive Solar Observation Data via Deep Learning-based Lossless Compression

Effective data compression technology is essential for addressing data storage and transmission needs, especially given the escalating volume and complexity of data generated by contemporary astronomy. In this study, we propose utilizing deep learning-based lossless compression techniques to improve compression efficiency. We begin with a qualitative and quantitative analysis of the temporal and spatial redundancy in solar observation data. Based on this analysis, we introduce a novel deep learning-based framework called AstroDLLC for the lossless compression of astronomical solar images. AstroDLLC first segments high-resolution images into blocks to ensure that deep learning model training does not rely on high-computation power devices. It then addresses the non-normality of the partitioned data through simple reversible computational methods. Finally, it utilizes Bit-swap to train deep learning models that capture redundant features across multiple image frames, thereby enhancing compression efficiency. Comprehensive evaluations using data from the New Vacuum Solar Telescope reveal that AstroDLLC achieves a maximum compression ratio of 3.00 per image, surpassing Gzip, RICE, and other lossless technologies. The performance of AstroDLLC underscores its potential to address data compression challenges in astronomy.

Analyzing the Trade-offs in Lossless Image Compression Techniques:Insights for Computer Science Research

Analyzing the Trade-offs in Lossless Image Compression Techniques:Insights for Computer Science Research

Joint Lossless Compression and Encryption for Medical Images

Joint Lossless Compression and Encryption for Medical Images

47‐4: A Novel Demura Compensation Data Compression Algorithm based on JPEG‐LS

JPEG‐LS is a lossless/near‐lossless image compression algorithm based on context modeling, which has the advantages of easy implementation, low resource consumption and high compression rate. It is widely used in the compression field of continuous‐tone still images. However, single image compression has significant latency and high hardware resource consumption issues, as the JPEG‐LS algorithm requires pixel by pixel prediction and real‐time context updates during the compression process, which is not conducive to algorithm IP implementation. Based on Demura compensation data compression requirements, this article has made changes to the pixel prediction method of the JPEG‐LS algorithm and delayed the update of the context to solve the compression latency and resource consumption issues. After algorithm optimization (NJPEGLS), the linebuffer resource occupation was reduced by 8/9, the clock frequency reached above 140 MHz, and the comprehensive compression rate loss was within 4.5%, meeting the demand for Demura compensation data compression/decompression. In this paper, we also conducted statistics and analysis on the distribution of values, and found that the distribution of values was very regular, and we propose an adaptive value scheme (KNJPEG‐LS) to further simplify the hardware circuit.

An Integrated Approach for Lossless Image Compression Using CLAHE, Two-Channel Encoding and Adaptive Arithmetic Coding

An Integrated Approach for Lossless Image Compression Using CLAHE, Two-Channel Encoding and Adaptive Arithmetic Coding

Real-time lossless image compression by dynamic Huffman coding hardware implementation

Real-time lossless image compression by dynamic Huffman coding hardware implementation

LFC-UNet: learned lossless medical image fast compression with U-Net.

In the field of medicine, the rapid advancement of medical technology has significantly increased the speed of medical image generation, compelling us to seek efficient methods for image compression. Neural networks, owing to their outstanding image estimation capabilities, have provided new avenues for lossless compression. In recent years, learning-based lossless image compression methods, combining neural network predictions with residuals, have achieved performance comparable to traditional non-learning algorithms. However, existing methods have not taken into account that residuals often concentrate excessively, hindering the neural network's ability to learn accurate residual probability estimation. To address this issue, this study employs a weighted cross-entropy method to handle the imbalance in residual categories. In terms of network architecture, we introduce skip connections from U-Net to better capture image features, thereby obtaining accurate probability estimates. Furthermore, our framework boasts excellent encoding speed, as the model is able to acquire all residuals and residual probabilities in a single inference pass. The experimental results demonstrate that the proposed method achieves state-of-the-art performance on medical datasets while also offering the fastest processing speed. As illustrated by an instance using head CT data, our approach achieves a compression efficiency of 2.30 bits per pixel, with a processing time of only 0.320 seconds per image.

A lossless compression and encryption scheme for sequence images based on 2D-CTCCM, MDFSM and STP

A lossless compression and encryption scheme for sequence images based on 2D-CTCCM, MDFSM and STP

Intelligent visually lossless compression of dental images

Background: Tendencies to increase the mean size of dental images and the number of images acquired daily makes necessary their compression for efficient storage and transferring via communication lines in telemedicine and other applications. To be a proper solution, lossy compression techniques have to provide a visually lossless option (mode) where a desired quality (invisibility of introduced distortions for preserving diagnostically valuable information) is ensured quickly and reliably simultaneously with a rather large compression ratio.Objective: Within such an approach, our goal is to give answers to several practical questions such as what encoder to use, how to set its parameter that controls compression, how to verify that we have reached our ultimate goal, what are additional advantages and drawbacks of a given coder, and so on.Methods: We analyze the performance characteristics of several encoders mainly based on discrete cosine transform for a set of 512 × 512 pixel fragments of larger size dental images produced by Morita and Dentsply Sirona imaging systems. To control the visual quality of compressed images and the invisibility of introduced distortions, we have used modern visual quality metrics and distortion invisibility thresholds established for them in previous experiments. Besides, we have also studied the so-called just noticeable distortions (JND) concept, namely, the approach based on the first JND point when the difference between an image subject to compression and its compressed version starts to appear.Results: The rate-distortion dependences and coder setting parameters obtained for the considered approaches are compared. The values of the parameters that control compression (PCC) have been determined. The ranges of the provided values of compression ratio have been estimated and compared. It is shown that the provided CR values vary from about 20 to almost 70 for modern coders and almost noise-free images that is significantly better than for JPEG. For images with visible noise, the minimal and maximal values of produced CR are smaller than for the almost noise-free images. We also present the results of the verification of compressed image quality by specialists (professional dentists).Conclusion: It is shown that it is possible and easy to carry out visually lossless compression of dental images using the proposed approaches with providing quite high compression ratios without loss of data diagnostic value.

Material mapping strategy to identify the density-dependent properties of dry natural snow

The mechanical properties of natural snow play a crucial role in understanding glaciers, avalanches, polar regions, and snow-related constructions. Research has concentrated on how the mechanical properties of snow vary, primarily with its density; the integration of cutting-edge techniques like micro-tomography with traditional loading methods can enhance our comprehension of these properties in natural snow. This study employs μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}CT imaging and uniaxial compression tests, along with the Digital Volume Correlation (DVC) to investigate the density-dependent material properties of natural snow. The data from two snow samples, one initially non-compressed (test 1) and the other initially compressed (test 2), were fed into Burger’s viscoelastic model to estimate the material properties. μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu$$\\end{document}CT imaging with 801 projections captures the three-dimensional structure of the snow initially and after each loading step at -18∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${^\\circ }$$\\end{document}C, using a constant deformation rate (0.2 mm/min). The relative density of the snow, ranging from 0.175 to 0.39 (equivalent to 160–360 kg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document}), is determined at each load step through binary image segmentation. Modulus and viscosity terms, estimated from Burger’s model, exhibit a density-dependent increase. Maxwell and Kelvin–Voigt moduli range from 0.5 to 14 MPa and 0.1 to 0.8 MPa, respectively. Viscosity values for the Maxwell and Kelvin–Voigt models vary from 0.2 to 2.9 GPa-s and 0.2 to 2.3 GPa-s within the considered density range, showing an exponent between 3 and 4 when represented as power functions. Initial grain characteristics for tests 1 and 2, obtained through image segmentation, reveal an average Specific Surface Area (SSA) of around 55 1/mm and 40 1/mm, respectively. The full-field strain distribution in the specimen at each load step is calculated using the DVC, highlighting strong strain localization indicative of non-homogeneous behavior in natural snow. These findings not only contribute to our understanding of natural snow mechanics but also hold implications for applications in fields such as glacier dynamics and avalanche prediction.

Multifocal multiview imaging and data compression based on angular-focal-spatial representation.

Multifocal multiview (MFMV) is an emerging high-dimensional optical data that allows to record richer scene information but yields huge volumes of data. To unveil its imaging mechanism, we present an angular-focal-spatial representation model, which decomposes high-dimensional MFMV data into angular, spatial, and focal dimensions. To construct a comprehensive MFMV dataset, we leverage representative imaging prototypes, including digital camera imaging, emerging plenoptic refocusing, and synthesized Blender 3D creation. It is believed to be the first-of-its-kind MFMV dataset in multiple acquisition ways. To efficiently compress MFMV data, we propose the first, to our knowledge, MFMV data compression scheme based on angular-focal-spatial representation. It exploits inter-view, inter-stack, and intra-frame predictions to eliminate data redundancy in angular, focal, and spatial dimensions, respectively. Experiments demonstrate the proposed scheme outperforms the standard HEVC and MV-HEVC coding methods. As high as 3.693 dB PSNR gains and 64.22% bitrate savings can be achieved.