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Image compression is a widely used technique to reduce the spatial redundancy in images. Recently, learning based image compression has achieved significant progress by using the powerful representation ability from neural networks. However, the current learning based image compression methods suffer from the huge computational cost, which limits their capacity for practical applications. In this paper, we propose a unified framework called Efficient Deep Image Compression (EDIC) based on three new technologies, including a channel attention module, a Gaussian mixture model and a decoder-side enhancement module. Specifically, we design an auto-encoder style network for learning based image compression. To improve the coding efficiency, we exploit the channel relationship between latent representations by using the channel attention module. Besides, the Gaussian mixture model is introduced for the entropy model and improves the accuracy for bitrate estimation. Furthermore, we introduce the decoder-side enhancement module to further improve image compression performance. Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework (Lu et al. 2019) to further improve the video compression performance. Simultaneously, our EDIC method boosts the coding performance significantly while bringing slightly increased computational cost. More importantly, experimental results demonstrate that the proposed approach outperforms the current image compression methods and is up to more than 150 times faster in terms of decoding speed when compared with Minnen's method (Minnen et al. 2018). Moreover, we also evaluate the performance of the human-centric task (i.e., face recognition) by using different coding strategies. [ABSTRACT FROM AUTHOR]

Lossless image compression techniques shrink the image size to improve the transmission efficiency and reduce the occupied storage space while ensuring the quality of the image is lossless. Among them, the LOCO‐I/JPEG‐LS algorithm benefits high lossless compression ratio and low computational complexity and thus is widely used for various real‐time applications. However, due to the problems of the context dependency in the LOCO‐I, the parallelism in the algorithm is greatly constrained, which significantly limits the throughput and the real‐time performance of hardware implementations. Existing designs achieve more parallelism by using a lot of hardware costs or straightforward chunking with losing compression ratio. In order to trade off the parallelism and the compression ratio, this paper proposes a chunk‐oriented error modeling scheme for LOCO‐I, which enables parallelism in both compression and decompression and achieves a better compression ratio in chunks. Based on the optimized algorithm, a high‐throughput flexible lossless compression and decompression architecture (HFCD) is proposed, which achieves higher pixel per clock (PPC) with less hardware cost. Additionally, HFCD introduces a parameter sharing mechanism to enable random access of image chunks to improve the flexibility for decompression. Experimental results show that, compared with state‐of‐the‐art works, HFCD achieves 3.02–13.50 times improvement for the PPC of compression. For decompression, benefiting from our optimizations, HFCD achieves 22.4 times speedup compared to the software solution. [ABSTRACT FROM AUTHOR]

The surge in advanced imaging techniques has generated vast biomedical image data with diverse dimensions in space, time and spectrum, posing big challenges to conventional compression techniques in image storage, transmission, and sharing. Here, we propose an intelligent image compression approach with the first-proved semantic redundancy of biomedical data in the implicit neural function domain. This Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS) can notably improve the compression efficiency for arbitrary-dimensional image data in terms of compression ratio and fidelity. Moreover, with weight transfer and residual entropy coding strategies, it shows improved compression speed while maintaining high quality. SINCS yields high quality compression with over 2000-fold compression ratio on 2D, 2D-T, 3D, 4D biomedical images of diverse targets ranging from single virus to entire human organs, and ensures reliable downstream tasks, such as object segmentation and quantitative analyses, to be conducted at high efficiency. A semantic redundancy based implicit neural compression guided with saliency map approach can compress arbitrary-dimensional biomedical images of diverse targets ranging from single virus to entire human organs, and ensures reliable downstream tasks, such as object segmentation and quantitative analyses, to be conducted at high efficiency. [ABSTRACT FROM AUTHOR]

Quantum-dot cellular automata (QCA) are one of the most promising alternatives to traditional VLSI technology despite significant current obstacles. The QCA has the advantages of very low power dissipation, faster switching speed, and extremely low circuit area, which can be used in designing nano-scale image processing circuits. Morphological operations and processing of digital image processing is a significant topic for researchers because it is widely used for analyzing, enhancing, and modifying images to extract meaningful information or improve their visual quality. Image processing is also used for image retrieval and enhancement, image compression, object recognition, machine vision, and medical applications. QCA technology, as a new and leading technology with great potential, can play a fundamental role in morphological operations, processing digital images, image editing, medical imaging, facial recognition, and autonomous vehicles. In recent years, researchers in this field have presented many circuits, but they have many flaws in terms of speed, accuracy, and area consumption, and the need to create more efficient circuits is felt more than ever. Therefore, in this article, a new design for morphological operations and processing digital images is presented using QCA technology. This paper presents a new efficient QCA-based implementation of image processing based on the direct interactions between the QCA cells. This circuit uses two majority gates of five new inputs to produce the output and produces the desired output. In addition, a comparison and analysis of the area and clocking complexity, design cost, and energy dissipation through simulation using QCADesigner and QCADesigner-E are done. The results show that the presented circuit produces the expected and correct output results in 0.75 clock phases, and the obtained results show the high speed and low consumption space of the presented circuit. In addition, the presented circuit performs better than the previous best circuits in terms of quantum cost and delay, and according to the mentioned advantages, it can be used to improve and expand other circuits in image processing. [ABSTRACT FROM AUTHOR]

The emergence of deep learning has sparked notable strides in the quality of synthetic media. Yet, as photorealism reaches new heights, the line between generated and authentic images blurs, raising concerns about the dissemination of counterfeit or manipulated content online. Consequently, there is a pressing need to develop automated tools capable of effectively distinguishing synthetic images, especially those portraying faces, which is one of the most commonly encountered issues. In this work, we propose a novel approach to synthetic face discrimination, leveraging deep learning-based image compression and predominantly utilizing the quality metrics of an image to determine its authenticity. [ABSTRACT FROM AUTHOR]

The availability of a higher resolution fine spectral bandwidth in hyperspectral images (HSI) makes it easier to identify objects of interest in them. The inclusion of noise into the resulting collection of images is a limitation of HSI and has an adverse effect on post-processing and data interpretation. Denoising HSI data is thus necessary for the effective execution of post-processing activities like image categorization and spectral unmixing. Most of the existing models cannot handle many forms of noise simultaneously. When it comes to compression, available compression models face the problems of increased processing time and lower accuracy. To overcome the existing limitations, an image denoising model using an adaptive fusion network is proposed. The denoised output is then processed through a compression model which uses an optimized deep learning technique called "chaotic Chebyshev artificial hummingbird optimization algorithm-based bidirectional gated recurrent unit" (CCAO-BiGRU). All the proposed models were tested in Python and evaluated using the Indian Pines, Washington DC Mall and CAVE datasets. The proposed model underwent qualitative and quantitative analysis and showed a PSNR value of 82 in the case of Indian Pines and 78.4 for the Washington DC Mall dataset at a compression rate of 10. The study proved that the proposed model provides the knowledge about complex nonlinear mapping between noise-free and noisy HSI for obtaining the denoised images and also results in high-quality compressed output. [ABSTRACT FROM AUTHOR]

The security and privacy of digital images are a major concern in cyberspace. JPEG is the most widely used image compression standard and yet there are problems with format compatibility and file size preservation in most of the modern encryption schemes for JPEG images. To address these problems, we propose a novel properties-preserving encryption scheme that obfuscates JPEG images by scrambling AC and DC coefficients. The key observation is that an innovative structured permutation has a remarkable effect on enciphering the coefficients and simultaneously prevents value overflow as well as size increment. On this basis, we first leverage the row-wise reordering and reversing technique to disorganize the DC coefficients. Afterward, a series of groups of consecutive DC differential values are produced using adaptive group differentiation and each of them is independently permuted inside. Finally, we perform block-wise ACCs permutation and intra-block ACCs shuffling to further confuse the outline of the original image. Moreover, invariant features regarding the coefficients are incorporated in the key generation process to resist various cryptanalytic attacks. Experimental results demonstrate a significant improvement in terms of format compatibility and file size preservation, and verify the security against various attacks. [ABSTRACT FROM AUTHOR]

Multispectral images, with their unique three-dimensional characteristics, require specialized spatial-spectral feature extraction modules to achieve superior compression results. Current end-to-end compression frameworks underperform compared to advanced coding algorithms, primarily due to insufficient spectral feature extraction at high bit rates and challenges in guiding entropy coding. To address these issues, this paper proposes the Mixture Autoregressive Spectral Attention Network (MARSA-Net), featuring two attention mechanisms: Coor-Spec and LD-CAM, and an autoregressive component. Our evaluation on real datasets from satellites demonstrates MARSA-Net's superiority over traditional algorithms, including H.266/VVC, underlining its potential in multispectral image compression. This research contributes to improved compression methods and extends our understanding of spectral feature extraction in multispectral imagery. [ABSTRACT FROM AUTHOR]

This paper suggests a new image encryption algorithm based on block compressive sensing (BCS) and chaotic scrambling techniques. With compressive sensing (CS), signals can be sampled much lower than the Nyquist-Shannon rate while preserving their information content. BCS can be used as a one-step process by using a 3D logistic chaotic map, the measurement matrix is used as a secret key for encryption. BCS algorithm used an individual image reconstruction algorithm that leverages l_1norm minimization to promote signal sparsity, and a smoothing operator to enhance image quality. An encrypted image is first decomposed into three sub-images based on the tricolor theory; then, a discrete wavelet transform is used to sparsely process the three decomposed images. A 2D Henon map is used to scramble the compressed image after compression. This process further enhances the security of the system by increasing the complexity of the encryption process. The results showed that the proposed system provides better security and has a lower computational complexity than other methods. Furthermore, the proposed system is resistant to known attacks such as brute force and statistical attacks. [ABSTRACT FROM AUTHOR]

Driven by the unprecedented amount of data generated in the last few decades, data storage and communication are becoming more challenging. Although many approaches in data compression have been developed to alleviate these challenges, more efforts are still needed, especially for lossless image compression, which is a promising technique when critical information loss is not allowed. In this paper, we propose a new algorithm called the Lossless Image Compression Algorithm using a Column Subtraction model (LICACS). LICA-CS is efficient, low in complexity, decreases the image bit-depth, and enhances state-of-the-art performance. LICA-CS first implements a color transformation method as a pre-processing phase, which strategically minimizes inter-channel correlations to optimize compression outcomes. After that, a novel subtraction method was developed to compress the image data column-wise. We tackle the similarity and proximity of pixel values within adjacent columns, which offers a distinct advantage in reducing image size observing a significant size reduction of 71%. This is achieved through the subtraction of neighboring columns. The conducted experiments on colored images show that LICA-CS outperforms existing algorithms in terms of compression rate. Moreover, our method exhibited remarkable enhancements in execution time, with compression and decompression processes averaging 1.93 seconds. LICA-CS advances the state-of-the-art in lossless image compression, promising enhanced efficiency and effectiveness in image compression technologies. [ABSTRACT FROM AUTHOR]

Data hiding in digital images is an important cover communication technique. This paper studies the lossless data hiding in an image compression domain. We present a novel lossless data hiding scheme in vector quantization (VQ) compressed images using adaptive prediction difference coding. A modified adaptive index rearrangement (AIR) is presented to rearrange a codebook, and thus to enhance the correlation of the adjacent indices in the index tables of cover images. Then, a predictor based on the improved median edge detection is used to predict the indices by retaining the first index. The prediction differences are calculated using the exclusive OR (XOR) operation, and the vacancy capacity of each prediction difference type is evaluated. An adaptive prediction difference coding method based on the vacancy capacities of the prediction difference types is presented to encode the prediction difference table. Therefore, the original index table is compressed, and the secret data are embedded into the vacated room. The experimental results demonstrate that the proposed scheme can reduce the pure compression rate compared with the related works. [ABSTRACT FROM AUTHOR]

At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by multiple stakeholders. In recent years, deep-learning-based methods can automatically detect abnormal behaviors with surveillance video, and blockchain as a decentralized and tamper-resistant distributed ledger technology is very suitable as a tool for providing evidence when determining liability. In this paper, a trusted supervision paradigm for autonomous driving (TSPAD) based on multimodal data authentication is proposed. Specifically, this paradigm consists of a deep learning model for driving abnormal behavior detection based on key frames adaptive selection and a blockchain system for multimodal data on-chaining and certificate storage. First, the deep-learning-based detection model enables the quantification of abnormal driving behavior and the selection of key frames. Second, the key frame selection and image compression coding balance the trade-off between the amount of information and efficiency in multiparty data sharing. Third, the blockchain-based data encryption sharing strategy ensures supervision and mutual trust among the regulatory authority, the logistic platform, and the enterprise in the driving process. [ABSTRACT FROM AUTHOR]

The field of image processing has seen remarkable advancements over the past few decades, and Discrete Wavelet Transform (DWT) has emerged as a powerful tool within this domain. This review article provides a comprehensive overview of previously published works that focus on DWT’s application in image processing. DWT offers multi-resolution analysis capabilities, making it particularly useful for various image processing tasks such as de-noising, compression, enhancement, and feature extraction. This review explores the fundamental principles of DWT and its mathematical foundations. We delve into its advantages over traditional Fourier Transform methods, particularly its ability to handle non-stationary signals and provide both frequency and spatial information simultaneously. The article surveys the application of DWT in different image processing techniques. It discusses how DWT contributes to the efficiency and effectiveness of image compression algorithms, such as the JPEG 2000 standard, and its role in reducing noise while preserving important image features. [ABSTRACT FROM AUTHOR]

Due to the excellent results achieved by transformers in computer vision, more and more scholars have introduced transformers into the field of medical image segmentation. However, the use of transformers will make the model's parameters very large, which occupies a large amount of the computer's resources, making them very time-consuming during training. In order to alleviate this disadvantage, this paper explores a flexible and efficient search strategy that can find the best subnet from a continuous transformer network. The method is based on a learnable and uniform L1 sparsity constraint, which contains factors that reflect the global importance of the continuous search space in different dimensions, while the search process is simple and efficient, containing a single round of training. At the same time, in order to compensate for the loss of accuracy caused by the search, a pixel classification module is introduced into the model to compensate for the loss of accuracy in the model search process. Our experiments show that the model in this paper compresses 30% of the parameters and FLOPs used, while also showing a slight increase in the accuracy of the model on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) have given rise to numerous visual analytics applications at the edge of the Internet. The image is typically captured by cameras and then live-streamed to edge servers for analytics due to the prohibitive cost of running CNN on computation-constrained end devices. A critical component to ensure low-latency and accurate visual analytics offloading over low bandwidth networks is image compression which minimizes the amount of visual data to offload and maximizes the decoding quality of salient pixels for analytics. Despite the wide adoption, JPEG standards and traditional image compression techniques do not address the accuracy of analytics tasks, leading to ineffective compression for visual analytics offloading. Although recent machine-centric image compression techniques leverage sophisticated neural network models or hardware architecture to support the accuracy-bandwidth tradeoff, they introduce excessive latency in the visual analytics offloading pipeline. This article presents CICO, a Context-aware Image Compression Optimization framework to achieve low-bandwidth and low-latency visual analytics offloading. CICO contextualizes image compression for offloading by employing easily-computable low-level image features to understand the importance of different image regions for a visual analytics task. Accordingly, CICO can optimize the tradeoff between compression size and analytics accuracy. Extensive real-world experiments demonstrate that CICO reduces the bandwidth consumption of existing compression methods by up to 40% under comparable analytics accuracy. Regarding the low-latency support, CICO achieves up to a 2× speedup over state-of-the-art compression techniques. [ABSTRACT FROM AUTHOR]

Digital images play a particular role in a wide range of systems. Image processing, storing and transferring via networks require a lot of memory, time and traffic. Also, appropriate protection is required in the case of confidential data. Discrete atomic compression (DAC) is an approach providing image compression and encryption simultaneously. It has two processing modes: lossless and lossy. The latter one ensures a higher compression ratio in combination with inevitable quality loss that may affect decompressed image analysis, in particular, classification. In this paper, we explore the impact of distortions produced by DAC on performance of several state-of-the-art classifiers based on convolutional neural networks (CNNs). The classic, block-splitting and chroma subsampling modes of DAC are considered. It is shown that each of them produces a quite small effect on MobileNetV2, VGG16, VGG19, ResNet50, NASNetMobile and NASNetLarge models. This research shows that, using the DAC approach, memory expenses can be reduced without significant degradation of performance of the aforementioned CNN-based classifiers. [ABSTRACT FROM AUTHOR]

This work develops theories and algorithms for computing rank‐revealing QR factorizations (qRRQR) of quaternion matrices. First, by introducing a novel quaternion determinant, a quasi‐Cramer's rule is established to investigate the existence theory of the qRRQR factorization of an m×n$$ m\times n $$ quaternion matrix A$$ \boldsymbol{A} $$. The proposed theory provides a systematic approach for selecting k$$ k $$ linearly independent columns of A$$ \boldsymbol{A} $$ in order to ensure that the resulting diagonal blocks R11$$ {\boldsymbol{R}}_{11} $$, R22$$ {\boldsymbol{R}}_{22} $$ of the R‐factor possess rank revealing properties in both spectral and Frobenius norms. Secondly, by increasing the quaternion determinants of R11$$ {\boldsymbol{R}}_{11} $$ in each iteration by a factor of at least f−1$$ {f}^{-1} $$(0

AbstractBackgroundMethodResultsConclusionThe guidance for clinicians on who requires further investigation when suspecting cauda equina syndrome has become clearer in England following the publication of the national recommendations. This does not have specific advice for those patients who have had recent imaging with no compression, returning to a healthcare provider with a change in symptoms. These cases can cause difficulty for clinicians with limited access to imaging who can often be reassured by previous imaging.This study presents a retrospective review of the case notes of 45 patients presenting on two occasions to the same Emergency Department who underwent magnetic resonance imaging due to the clinical diagnosis of cauda equina syndrome. Those with compression of the cauda equina on the second visit were compared to those without a compressive cause for their symptoms.Patients presenting with an increased number of clinical symptoms associated with cauda equina syndrome on their return visit were more likely to have compression of the cauda equina on imaging.The small numbers in this study would suggest caution, however, if patients present with an increasing number of symptoms further imaging should be considered, even with previous reassuring scans. [ABSTRACT FROM AUTHOR]

Effectively compressing transmitted images and reducing the distortion of reconstructed images are challenges in image semantic communication. This paper proposes a novel image semantic communication model that integrates a dynamic decision generation network and a generative adversarial network to address these challenges as efficiently as possible. At the transmitter, features are extracted and selected based on the channel's signal-to-noise ratio (SNR) using semantic encoding and a dynamic decision generation network. This semantic approach can effectively compress transmitted images, thereby reducing communication traffic. At the receiver, the generator/decoder collaborates with the discriminator network, enhancing image reconstruction quality through adversarial and perceptual losses. The experimental results on the CIFAR-10 dataset demonstrate that our scheme achieves a peak SNR of 26 dB, a structural similarity of 0.9, and a compression ratio (CR) of 81.5% in an AWGN channel with an SNR of 3 dB. Similarly, in the Rayleigh fading channel, the peak SNR is 23 dB, structural similarity is 0.8, and the CR is 80.5%. The learned perceptual image patch similarity in both channels is below 0.008. These experiments thoroughly demonstrate that the proposed semantic communication is a superior deep learning-based joint source-channel coding method, offering a high CR and low distortion of reconstructed images. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are a pivotal technology in today's intelligent transportation systems, improving traffic conditions by enhancing road safety, reducing congestion, conserving energy, decreasing emissions, and boosting travel efficiency and comfort. The key to achieving these objectives is to enable autonomous vehicles to perceive their surroundings in real time, with accuracy and stability, and to make appropriate decisions and controls on the basis of this perception. This perception relies on onboard sensors such as cameras, radars, lidars, and ultrasonic sensors. Radar sensors, one of the most frequently used perception devices in autonomous vehicles, are known for their strong anti-interference ability, effectiveness under occlusion, accurate range measurement, and wide coverage. However, radar sensors are limited by low resolution, high cost, large data volume, and difficulty in identifying object types and colors. Therefore, compensating for the inadequacies of radar sensing technology and enhancing the perceptual abilities of autonomous vehicles are ongoing challenges. In this paper, we introduce a novel image signal-based perception and transmission technology, aimed at overcoming the limitations of radar sensing and improving the perception efficiency and quality of autonomous vehicles. This technology employs image signals as the information medium, transmitting road information from the perception end to the processing end via wireless communication, thus enabling road condition perception. Compared with radar sensing technology, this image-based perception transmission technology is more cost-effective, as it only requires standard cameras and eliminates the need for expensive radar equipment. It captures a richer array of information, including object shape, size, position, direction, color, and texture, rather than just distance and velocity. The processing is more flexible, utilizing existing image processing and machine learning technologies for image signal compression, encoding, decoding, recognition, and analysis, thereby enhancing the accuracy and real-time capabilities of perception. [ABSTRACT FROM AUTHOR]

The surge in image data has significantly increased the pressure on storage and transmission, posing new challenges for image compression technology. The structural texture of an image implies its statistical characteristics, which is effective for image encoding and decoding. Consequently, content-adaptive compression methods based on learning can better capture the content attributes of images, thereby enhancing encoding performance. However, learned image compression methods do not comprehensively account for both the global and local correlations among the pixels within an image. Moreover, they are constrained by rate-distortion optimization, which prevents the attainment of a compact representation of image attributes. To address these issues, we propose a syntax-guided content-adaptive transform framework that efficiently captures image attributes and enhances encoding efficiency. Firstly, we propose a syntax-refined side information module that fully leverages syntax and side information to guide the adaptive transformation of image attributes. Moreover, to more thoroughly exploit the global and local correlations in image space, we designed global–local modules, local–global modules, and upsampling/downsampling modules in codecs, further eliminating local and global redundancies. The experimental findings indicate that our proposed syntax-guided content-adaptive image compression model successfully adapts to the diverse complexities of different images, which enhances the efficiency of image compression. Concurrently, the method proposed has demonstrated outstanding performance across three benchmark datasets. [ABSTRACT FROM AUTHOR]

The Karhunen-Loève transform (KLT) stands as a well-established discrete transform, demonstrating optimal characteristics in data decorrelation and dimensionality reduction. Its ability to condense energy compression into a select few main components has rendered it instrumental in various applications within image compression frameworks. However, computing the KLT depends on the covariance matrix of the input data, which makes it difficult to develop fast algorithms for its implementation. Approximations for the KLT, utilizing specific rounding functions, have been introduced to reduce its computational complexity. Therefore, our paper introduces a category of low-complexity, data-independent KLT approximations, employing a range of round-off functions. The design methodology of the approximate transform is defined for any block-length N , but emphasis is given to transforms of N = 8 due to its wide use in image and video compression. The proposed transforms perform well when compared to the exact KLT and approximations considering classical performance measures. For particular scenarios, our proposed transforms demonstrated superior performance when compared to KLT approximations documented in the literature. We also developed fast algorithms for the proposed transforms, further reducing the arithmetic cost associated with their implementation. Evaluation of field programmable gate array (FPGA) hardware implementation metrics was conducted. Practical applications in image encoding showed the relevance of the proposed transforms. In fact, we showed that one of the proposed transforms outperformed the exact KLT given certain compression ratios. [ABSTRACT FROM AUTHOR]

In this work, the deformation behavior of porous ceramics ZrO2–5.5 wt.% Y2O3 with different morphology of the pore space under axial quasi-static compression is studied by the digital image correlation (DIC) method. Two stages of the deformation behavior of the ceramics have been identified, namely, the controlled accumulation of microcracks and the formation of block structures. It has been found that an increase in the average pore size leads to a decrease in the tensile strength, a decrease in the effective elasticity modulus, and an increase in the ultimate deformation, while the nature of the deformation macrolocalization changes from ordered to chaotic one. It is shown that the rate of accumulation of local deformations in the central part of the deformable sample with a pore size of 68 μm is several times higher than in the sample with a pore size of 29 μm. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for calculating the Euler characteristic in 2D binary images. The problem is addressed using the Three OrThogonal symbol chain code (3OT code), using only one symbol for the calculation of the Euler characteristic. Using this code, it is possible to introduce new geometric concepts represented by the same symbol of the 3OT alphabet and to simplify the overall equation of the Euler characteristic. This process is supported by the proof of a set of theorems and their numerical validation, using a set of binary images with a variable number of holes. Thus, this research proves that the 3OT code can be used not only for image compression as reported in the literature, but also to simplify the expression of the Euler characteristic as well as for the analysis and simplification of the shape of contours. [ABSTRACT FROM AUTHOR]

In the task of power line inspection, Unmanned Aerial Vehicles (UAVs) are frequently used for capturing images. With the rapid advancement of sensor technology, the spatial, radiometric, and spectral resolutions of UAV images are constantly improving, leading to an increased storage requirement for individual images. Given that UAVs usually operate with limited computational resources, transmission capability and storage space, there are significant challenges in image compression, storage and transmission. This underscores the importance of a high-performance image compression technique. To solve the above problem, we unveil a compression strategy for images that have been acquired through learning utilizing discrete Gaussian mixture-based probability distributions to increase the efficiency of image compression and the fidelity of reconstruction. In addition, to speed up decoding, we employ a parallel context model, which facilitates decoding in a highly parallel manner. Experimental evidence indicates that our approach attains performance that is at the forefront of the field while significantly expediting the decoding process (speeding up the decoding process by more than 49.78%) in our experiments, outpacing traditional coding standards and existing learned compression approaches by 5.75 dB and 1.23 dB in PSNR. [ABSTRACT FROM AUTHOR]

The discrete cosine transform (DCT) is widely used for image and video compression. Lossy algorithms such as JPEG, WebP, BPG and many others are based on it. Multiple modifications of DCT have been developed to improve its performance. One of them is adaptive DCT (ADCT) designed to deal with heterogeneous image structure and it may be found, for example, in the HEVC video codec. Adaptivity means that the image is divided into an uneven grid of squares: smaller ones retain information about details better, while larger squares are efficient for homogeneous backgrounds. The practical use of adaptive DCT algorithms is complicated by the lack of optimal threshold search algorithms for image partitioning procedures. In this paper, we propose a novel method for optimal threshold search in ADCT using a metric based on tonal distribution. We define two thresholds: pm, the threshold defining solid mean coloring, and ps, defining the quadtree fragment splitting. In our algorithm, the values of these thresholds are calculated via polynomial functions of the tonal distribution of a particular image or fragment. The polynomial coefficients are determined using the dedicated optimization procedure on the dataset containing images from the specific domain, urban road scenes in our case. In the experimental part of the study, we show that ADCT allows a higher compression ratio compared to non-adaptive DCT at the same level of quality loss, up to 66% for acceptable quality. The proposed algorithm may be used directly for image compression, or as a core of video compression framework in traffic-demanding applications, such as urban video surveillance systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for designing non-stationary biorthogonal wavelets using normality and biorthogonality conditions of nonstationary multiresolution analysis. Non-stationary biorthogonal wavelets are effective in reducing image size while preserving quality, and can capture localized features accurately, making them a popular choice in image compression algorithms. [ABSTRACT FROM AUTHOR]

Efficient compression algorithms are required to handle the growing amount of medical picture data, ensuring that storage and transmission requirements are met without compromising diagnostic quality. This research presents a hybrid image compression framework that integrates deep learning alongside standard lossless compression techniques. A convolutional autoencoder (CAE) learns a compact representation of medical images, which are subsequently compressed using the Brotli algorithm. Our technique beats conventional approaches, like JPEG, JPEG2000, and wavelet-based ones, according to an analysis of a brain MRI dataset. It maintains competitive compression ratios while producing higher (PSNR) and (MSE), indicating higher picture integrity and low information loss. To strike a good balance between the critical need for accurate diagnosis and the economical use of resources, this study offers a possible method for compressing medical images. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a novel deep image coding and decoding model for GMMQI (Gaussian mixture model quartile intervals, GMMQI) and a variable rate bit allocation method to optimize the rate distortion performance and prioritize the transmissions of more significant information. First, to address the problem of bit streams leading to ambiguity during encoding and decoding, we convert the image into a potential tensor, each element of which uses a four-bit parameter dictionary to preserve the parameter bits. Then, the variable rate is calculated using quaternions based on the parameter dictionary, and a hybrid CLM &BAM (Channel latent map & Bit allocation map) approach is designed to assign bits to the potential tensor and encode it, which transforms the problem of finding the optimal encoder-decoder into finding the optimal hyper-parameters in the model and reduces the complexity of the GMMQI model. Finally, a GMMQI approach with variable rate bit allocation is developed in combination with CLM &BAM, to be able to prioritize the transmission of more significant information. The experimental results show that the GMMQI method reaches an advanced level compared to the traditional image compression standards BPG, JPEG2000, and JPEG, and is comparable to the most advanced level compared to the existing deep learning based compression methods. [ABSTRACT FROM AUTHOR]

Quantum computing has emerged as a transformative paradigm, with revolutionary potential in numerous fields, including quantum image processing and compression. Applications that depend on large scale image data could benefit greatly from parallelism and quantum entanglement, which would allow images to be encoded and decoded with unprecedented efficiency and data reduction capability. This paper provides a comprehensive overview of the rapidly evolving field of quantum image compression, including its foundational principles, methods, challenges, and potential uses. The paper will also feature a thorough exploration of the fundamental concepts of quantum qubits as image pixels, quantum gates as image transformation tools, quantum image representation, as well as basic quantum compression operations. Our survey shows that work is still sparse on the practical implementation of quantum image compression algorithms on physical quantum computers. Thus, further research is needed in order to attain the full advantage and potential of quantum image compression algorithms on large-scale fault-tolerant quantum computers. [ABSTRACT FROM AUTHOR]

Today, managing a large amount of information becomes increasingly crucial. Efficient storage and retrieval of digital data are essential for their effective utilization. This study investigates the efficacy of Spatial Domain Image Compression Techniques, which directly manipulate the original image to reduce its size by leveraging pixel spatial relationships. These techniques segment the image into blocks and process each block independently. Evaluation entails measuring perceptual quality through metrics, such as PSNR, WPSNR, NMSE, and SSIM applied to the compressed image. Experimental results provide a comparative analysis of the performance of these techniques. [ABSTRACT FROM AUTHOR]

Infrared image enhancement technology plays a crucial role in improving image quality, addressing issues like low contrast, lack of sharpness, and poor visual effects within the original images. However, existing decomposition-based algorithms struggle with balancing detail enhancement, noise suppression, and utilizing global and local information effectively. This paper proposes an innovative method for enhancing details in infrared images using adaptive guided filtering and global–local mapping. Initially, the original image is decomposed into its base layer and detail layer through the adaptive guided filter and difference of the Gaussian filter. Subsequently, the detail layer undergoes enhancement using a detail gain factor. Finally, the base layer and enhanced detail layer are merged and remapped to a lower gray level. Experimental results demonstrate significant improvements in global and local contrast, as well as sharpness, with an average gradient enhancement of nearly 3% across various scenes. [ABSTRACT FROM AUTHOR]

Although deep learning technique has achieved significant improvement on image compression, but its advantages are not fully explored in video compression, which leads to the performance of deep-learning-based video compression (DLVC) is obviously inferior to that of hybrid video coding framework. In this article, we proposed a novel network to improve the performance of DLVC from its most important modules, including Motion Process (MP), Residual Compression (RC), and Frame Reconstruction (FR). In MP, we design a split second-order attention and multi-scale feature extraction module to fully remove the warping artifacts from multi-scale feature space and pixel space, which can help reduce the distortion in the following process. In RC, we propose a channel selection mechanism to gradually drop redundant information while preserving informative channels for a better rate-distortion performance. Finally, in FR, we introduce a residual multi-scale recurrent network to improve the quality of the current reconstructed frame by progressively exploiting temporal context information between it and its several previous reconstructed frames. Extensive experiments are conducted on the three widely used video compression datasets (HEVC, UVG, and MCL-JVC), and the performance demonstrates the superiority of our proposed approach over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The proliferation of remote sensing image data in recent years has posed a pressing need for efficient compression techniques due to constrained transmission bandwidth. While lossless compression preserves image fidelity, it falls short of meeting real-time demands. Conversely, conventional lossy compression methods can attain high compression ratios for real-time applications, but often introduce issues like block artifacts, blurring, and distortions in the decompressed images. Hence, we propose the Global Feature-Assisted Segmentation Compression Network (GFSCompNet) as a solution for high compression ratio lossy compression. Initially, we design a segmentation network utilizing a dual-branch global feature-assisted segmentation approach to precisely detect small targets in remote sensing images. On the compression side of the network, we leverage an attention mechanism and code rate allocation technique to seamlessly merge the segmented small target information with the original image, thereby allocating a higher compression code rate to the small target region. Furthermore, a joint hyper-priority decoding and entropy coding estimation network is proposed to further remove the redundancy in the potential representation and improve the compression ratio. Experimental results conducted under conditions of high compression ratios and comparable bit rates demonstrate that our approach yields higher-quality reconstructed images compared to the JPEG algorithm and outperforms other deep learning-based image compression methods. Additionally, it effectively preserves small target information, thereby enhancing the interpretability of machine learning models. [ABSTRACT FROM AUTHOR]

Detecting sources in digital images and videos is crucial to multimedia forensics research. The inherent physical properties of imaging sensors result in the presence of Photo Response Non-Uniformity Noise (PRNU) within the captured multimedia content. This particular noise, often called the "fingerprint," is a unique and stable feature for identifying the source camera. However, the compression of images by platform codecs on social network media introduces varying degrees of quantization noise, making to address this issue, a noise extractor based on variance stabilizing transform and adaptive block clustering principal component analysis (PCA) is proposed, along with an enhanced processing model that incorporates cyclic residual recycling. Firstly, the GAT and adaptive block clustering PCA filtering are applied to extract noises from the images. The obtained noises are then subjected to zero-mean and diagonal artifact elimination processing. Next, the real and imaginary parts of the noise spectrum are individually subjected to real-time iterative least squares smoothing based on half-quadratic optimization. Due to the presence of PRNU information in the residual between the pre-smoothed and post-smoothed signals, additional cyclic smoothing is applied to refine the signal further. Finally, the smoothed signals are accumulated to obtain the enhanced PRNU noise. Experimental comparisons conducted on the public dataset Dresden demonstrate that the proposed model significantly outperforms existing methods in terms of source camera identification for low-resolution and strong JPEG compression images. [ABSTRACT FROM AUTHOR]

The problem of layout segmentation is still very challenging in document images like newspapers, magazines, and research articles, that have both text and non-text components arranged in an artistic way to attract various types of readers. Traditionally, layout segmentation has been carried out in the pixel domain, with an assumption that images are always available in the uncompressed pixel form. However, in reality, the images are acquired and rendered in the compressed form, and therefore the traditional techniques require additional stage of decompression to get back the images in the pixel form for further processing. Therefore, in this research paper, the idea of direct layout segmentation in compressed document images is proposed, to bypass the decompression stage and at the same time provide good performance with reduced computation time. This paper proposes to explore a novel deep learning architecture called as DCT-CompSegNet, that learns features straight from the DCT compressed streams of JPEG documents to accomplish layout segmentation directly in the JPEG compressed domain. Unlike the existing layout segmentation methods that work in pixel domain, the novelty here is that a compressed stream of DCT coefficients extracted from the JPEG documents is used to train the deep learning network. The feature learning in the model is so efficient that it is capable of accomplishing layout segmentation in both printed as well as handwritten document images with state-of-the-art performance. Experiments have been carried out using two benchmark datasets - Publay and Prima consisting of complex machine-printed document images, and the robustness of the model is also demonstrated with the self-created Handwritten dataset. [ABSTRACT FROM AUTHOR]

Local features of an image provide a robust way of image matching if they are invariant to large variations in scale, viewpoint, illumination, rotation, and affine transformations. In this paper, we propose a novel feature descriptor based on circular and elliptical local sampling of image pixels to attain fast and robust results under varying imaging conditions. The proposed descriptor is tested on a standard benchmark dataset comprising of images with varying imaging conditions and compression quality. Results show that the proposed method generates sufficient or more number of stable and correct matches between an image pair (original image and distorted image) as compared to SIFT with a speedup of 1.6 on average basis. The paper also discusses the reason of choosing SIFT descriptor for comparison and its efficacy in different scenarios. The paper also reasons the robustness of hand crafted feature descriptors and why they hold an upper hand among many other deep learning methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction/purpose: This paper presents the results of the research on visually lossless image compression which is of particular interest because it achieves a high degree of compression, while the visual quality of the image is not impaired, i.e., end users are very satisfied with the image quality. The analysis was carried out using the publicly available large-scale picture-wise KonJND-1k database which contains the results of subjective tests on JPEG and BPG compressed images. Methods: Thanks to the availability of images from the KonJND-1k database, the dependence of objective assessments of image quality on parameters that control the degree of compression of source signals (quality factor for JPEG and quantization parameter for BPG) is analyzed. The results of the visually lossless subjective tests are used for a deep analysis of the boundary and typical values of the parameters that control these two types of compression, as well as for the analysis of the corresponding values of the objective quality scores. Furthermore, reliable features for predicting the boundary between visually lossless and visually lossy compression have been identified. For that purpose, the degree of agreement between the predictions and the ground truth values of the peak signal-to-noise ratio (PSNR) and image representation in bits per pixel (bpp) is used. The visually lossless compression ratio is used to compare JPEG and BPG techniques. Results: It is shown that the boundary between visually lossless and visually lossy image compression is found in a wide range of PSNR values (about 20 dB for JPEG and 15 dB for BPG). The corresponding JPEG image compression quality factor values at this threshold also range widely from 31 to 79, with concentration between 40 and 45. For the BPG encoder, the values of the quantization parameter are grouped around 30, and the boundary values are 25 and 34. Furthermore, it is shown that this boundary can be reliably determined based on simple features derived from the original uncompressed image. Gradient-based features known as spatial frequency and spatial information proved to be the best predictors. The degree of agreement between the predictions obtained from these features with the ground truth values of PSNR and bpp in both types of compression is greater than 85%. A comparative analysis has showed that, using BPG compression, it is possible, on the average, to achieve a twice larger compression ratio of visually lossless compression than for JPEG (80 versus 40). Conclusion: Although a high degree of agreement is achieved between the predictions and the ground truth values of PSNR and bpp of the boundary between visually lossless and visually lossy compression, there is a need for the development of new prediction approaches, especially with the BPG technique, which through the compression ratio proved to be superior to the JPEG technique. The existing databases used for the analysis of visually lossless compression contain color images from the visible part of the electromagnetic spectrum. Considering the increasing use of images from the infrared part of the spectrum, there is a need to conduct similar tests in this spectral range. [ABSTRACT FROM AUTHOR]

Fixed point theorems are very important tools in different branches of mathematics. In this paper, we introduce partial uniform spaces as a generalization of metric spaces; and study some basic properties. Various examples support the theory. We prove fixed point theorems for H-partial uniform spaces by using a map called an E-distance function. Finally, we give the applications of these fixed point theorems to compress digital images. [ABSTRACT FROM AUTHOR]

As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform—1D, 2D, or 3D—would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics. [ABSTRACT FROM AUTHOR]

This paper introduces a new descriptor called orientation‐magnitude response maps as a single 2D image to effectively explore motion patterns. Moreover, boosted multi‐stream CNN‐based model with various attention modules is designed for human action recognition. The model incorporates a convolutional self‐attention autoencoder to represent compressed and high‐level motion features. Sequential convolutional self‐attention modules are used to exploit the implicit relationships within motion patterns. Furthermore, 2D discrete wavelet transform is employed to decompose RGB frames into discriminative coefficients, providing supplementary spatial information related to the actors actions. A spatial attention block, implemented through the weighted inception module in a CNN‐based structure, is designed to weigh the multi‐scale neighbours of various image patches. Moreover, local and global body pose features are combined by extracting informative joints based on geometry features and joint trajectories in 3D space. To provide the importance of specific channels in pose descriptors, a multi‐scale channel attention module is proposed. For each data modality, a boosted CNN‐based model is designed, and the action predictions from different streams are seamlessly integrated. The effectiveness of the proposed model is evaluated across multiple datasets, including HMDB51, UTD‐MHAD, and MSR‐daily activity, showcasing its potential in the field of action recognition. [ABSTRACT FROM AUTHOR]

In recent years, internet of underwater things (IoUT) has become a hot topic of research and attracted many researchers to use them for applications such as underwater environment monitoring and exploration, monitoring geological and biological changes, defense, disaster monitoring and prevention. Smart underwater objects in IoUT are generally equipped with high definition cameras for capturing underwater images. Storage and transmission of high-quality images captured by these smart objects is a challenging task due to huge volumes of data generated. Compressive sensing (CS) based data sampling and acquisition strategy provides promising ways to compress image at low computational cost and power. However, the samples are chosen randomly compromising reconstructed image's visual quality. To select signal samples with more importance, Adaptive Block Compressive Sensing (ABCS) can be utilized. In this research paper, we implement two-modified fast haar wavelet transform (MFHWT) coefficient thresholding based ABCS strategies for compression of underwater surveillance images in IoUT. The novelty lies in the fact that the proposed strategies choose only 5–20% of samples/measurements to achieve a rise in PSNR of about 3-5dB with respect to conventional techniques. The results show that the proposed strategies have NCC and SSIM values closer to 1 and NAE values closer to 0. Also, around 70% memory space can be saved. [ABSTRACT FROM AUTHOR]

The characterisation of edges in medical images is critical for disease diagnosis. However, existing systems are still deficient in this task. Traditionally, integer-based derivative operators are employed due to their efficiency in time complexity but lack the ability to track nonlocal and non-singular edge maps. This study proposes a new mask based on Atangana-Beleanu fractional operator. This operator has the same complexity as the state-of-the-art integer-order derivative mask known as the Canny edge detector but has the added advantage to characterise more efficiently nonlocal and nonsingular edge maps. Performance evaluation of the proposed mask reveals an enhanced performance in the context of robustness to noise and quality edge extraction, a significant contribution to literature. The metric for the study is the signal-to-noise ratio, and the structural similarity index and appropriate mask observed is a mask of dimension greater than five. [ABSTRACT FROM AUTHOR]

In insecure communication environments where the communication bandwidth is limited, important image data must be compressed and encrypted for transmission. However, existing image compression and encryption algorithms suffer from poor image reconstruction quality and insufficient image encryption security. To address these problems, this paper proposes an image-compression and encryption scheme based on a newly designed hyperchaotic system and two-dimensional compressed sensing (2DCS) technique. In this paper, the chaotic performance of this hyperchaotic system is verified by bifurcation diagrams, Lyapunov diagrams, approximate entropy, and permutation entropy, which have certain advantages over the traditional 2D chaotic system. The new 2D chaotic system as a pseudo-random number generator can completely pass all the test items of NIST. Meanwhile, this paper improves on the existing 2D projected gradient (2DPG) algorithm, which improves the quality of image compression and reconstruction, and can effectively reduce the transmission pressure of image data confidential communication. In addition, a new image encryption algorithm is designed for the new 2D chaotic system, and the security of the algorithm is verified by experiments such as key space size analysis and encrypted image information entropy. [ABSTRACT FROM AUTHOR]

Nowadays, it is required to transmit images and videos underwater with high speed and good quality. Many applications and activities in underwater environments need modern and modified techniques. Moreover, robots with macro size underwater and with micro size inside the body need high-speed communication with the control station. In this paper, we survey some of the available tools for underwater acoustic communication, the trends and difficulties in this area. We survey the characteristics of Orthogonal Frequency Division Multiplexing (OFDM) and Single-Carrier Frequency Division Multiple Access (SC-FDMA) in underwater acoustic image communication. In addition, a model for image compression to increase the data rate of transmission under water is presented. It achieves a Compression Ratio (CR) of about 14 and gives better-decoded images with a Peak Signal-to-Noise Ratio (PSNR) up to 34 dB. A comparison between Zero-Forcing (ZF) and pre-ZF equalization formats for Multi-Input Multi-Output (MIMO) OFDM used for image transmission is presented with some results. Below a Signal-to-Noise Ratio (SNR) of 5 dB over the communication channel, the Bit Error Rate (BER) difference between the MIMO OFDM with ZF equalization and the MIMO OFDM with ZF pre-equalization is not high. At an SNR of more than 10 dB, the difference is very high. The ZF pre-equalization for MIMO OFDM improves the system performance compared to the case of ZF post-equalization due to the elimination of noise enhancement. [ABSTRACT FROM AUTHOR]

We consider a single server queueing model with three service types, each with three phases, where customers arrive according to a Poisson process and the service time of each customer follows a Hyper-Erlang distribution. This study aims to analyze the impact of interruptions, such as breakdowns, repairs, and single working vacations, on the performance of the queueing system. The standby server activates whenever the main server experiences a breakdown. Using the matrix geometric method, we examine the steady-state probability vector of the model. Our results demonstrate the impact of these interruptions on system performance measures and provide a cost analysis. Finally, we present numerical examples to illustrate the practical applications of our findings in an image compression system. [ABSTRACT FROM AUTHOR]

Deep neural networks based on hyper-encoders play a critical role in estimating prior distributions in remote sensing image compression issues. However, most of the existing encoding methods suffer from a problem on the hyper-encoding side, namely the mismatch of extraction ability with the encoder. This ability bias results in likelihood features that fail to extract sufficient information from latent representations. To solve this problem, the feature extraction capabilities of the hyper-encoder are enhanced to better estimate the Gaussian likelihood of the latent representation in end-to-end network optimization. Specifically, residual blocks and a parameter estimation module are incorporated to balance the performance of the encoder and the hyper-encoder. Furthermore, it is observed that the well-trained compression model tends to generate a fixed pattern of latent representations. Therefore, we incorporate a nonlocal cross-channel graph (NCG) on the backside of the encoder. Specifically, it aggregates features between similar latent representations in a graphical manner to further enhance the side information extraction capability of the hyper-encoder. Considering the computational cost, a sparse graph strategy is further developed to dynamically select the most relevant latent representations for aggregation operations, which greatly reduces the computational effort. The proposed algorithm is named nonlocal cross-channel efficient graph (NCEG). A long-dependent residual network is selected as the backbone, and a sparse attention module is inserted into the encoder/decoder side to enhance the perceptual field of the network. The experimental results on two evaluation datasets demonstrate that the proposed method achieves satisfactory results compared to other learning-based methods. [ABSTRACT FROM AUTHOR]