Image Characteristics Research Articles

MRI Characteristics for Distinguishing Solitary Fibrous Tumor From Desmoid Tumor.

To elucidate the magnetic resonance imaging (MRI) characteristics for distinguishing solitary fibrous tumors (SFTs) from desmoid tumors (DTs). A retrospective study of 66 consecutive patients with histopathologically proven SFT (n = 34; 13 males and 21 females; mean age, 52.0 ± 17.1 years) or DT (n = 32; 11 males and 21 females; mean age, 39.0 ± 21.3 years) was conducted. The two groups were quantitatively compared in terms of the size, signal intensity ratio (SIR), and apparent diffusion coefficient value. For qualitative analysis, the tumor location, boundary, shape, internal uniformity, predominant signal intensity, T1-weighted images (T1WI) characteristics (hyperintense area), T2-weighted images (T2WI) characteristics (hypointense area, marked hyperintense area, flow void, band sign, and yin-yang sign), and contrast-enhanced T1WI characteristics (unenhanced area and degree of enhancement) were compared between the two groups. Multiple stepwise logistic regression analyses were conducted to distinguish between the SFT and DT. T1 (P = 0.010) and T2 (P = 0.026) SIRs were higher in SFTs than in DTs. Hyperintense areas on T1WI (P < 0.001), marked hyperintense areas on T2WI (P = 0.025), and flow void (P = 0.025) were more frequently noted in SFTs. On T1WI, the solid component predominantly revealed hyperintensity in SFTs and isointensity in DTs (P < 0.001). Indistinct tumor boundary (P < 0.001), hypointense area on T2WI (P < 0.001), and band sign (P < 0.001) were more frequently observed in DTs. Multiple stepwise logistic regression analysis revealed that the hyperintense area on T1WI (odds ratio favoring SFT, 12.80, P = 0.002) and band sign (odds ratio favoring DT, 0.03; P < 0.001) were independent predictors. MRI characteristics can help distinguish SFT from DT. The presence of a hyperintense area relative to the skeletal muscle on T1WI in SFTs and the band sign on T2WI in DTs are important MRI features.

Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network

The ripeness of crystal guava fruit is currently sorted conventionally by analyzing the colour of the rind visually with the human eye. However, this method has several weaknesses that result in low accuracy and inconsistency. Therefore, automatic determination of ripeness level is necessary to increase accuracy and obtain precise information. This research uses the HSI colour space as an interpretation of fruit image characteristics and uses the Backpropagation algorithm to perform classification. This study utilizes image data of crystal guava fruit, categorizing them into four stages of ripeness: unripe, half-ripe, ripe, and very ripe. There are 140 fruit image data with 35 data for each ripeness category. Each image will be processed with median filter, cropping and segmentation. The HSI value will be taken from the image and processed at the classification stage using the Backpropagation algorithm. In classification using Backpropagation Neural Network, the best network model in this study was achieved in the 3 10 4 network architecture with a binary sigmoid activation function, learning rate = 0.3, and batch size = 64. This model produces a loss value of 0.5364 with an accuracy of 0.9 in testing process.

JotlasNet: Joint tensor low-rank and attention-based sparse unrolling network for accelerating dynamic MRI.

Joint low-rank and sparse unrolling networks have shown superior performance in dynamic MRI reconstruction. However, existing works mainly utilized matrix low-rank priors, neglecting the tensor characteristics of dynamic MRI images, and only a global threshold is applied for the sparse constraint to the multi-channel data, limiting the flexibility of the network. Additionally, most of them have inherently complex network structure, with intricate interactions among variables. In this paper, we propose a novel deep unrolling network, JotlasNet, for dynamic MRI reconstruction by jointly utilizing tensor low-rank and attention-based sparse priors. Specifically, we utilize tensor low-rank prior to exploit the structural correlations in high-dimensional data. Convolutional neural networks are used to adaptively learn the low-rank and sparse transform domains. A novel attention-based soft thresholding operator is proposed to assign a unique learnable threshold to each channel of the data in the CNN-learned sparse domain. The network is unrolled from the elaborately designed composite splitting algorithm and thus features a simple yet efficient parallel structure. Extensive experiments on two datasets (OCMR, CMRxRecon) demonstrate the superior performance of JotlasNet in dynamic MRI reconstruction.

Theme park reviews: how text mining cognitive characteristics and emotions can determine theme park image

Graphical abstract Purpose The theme park industry has attracted wide attention and visitors’ perceptions are key to improving theme park management. Applying the cognitive-affective theory, this study aims to explore various cognitive attributes and affective attitudes and how they affect the overall theme park image. Design/methodology/approach A mixed research method was used to analyze tourists’ cognitive, affective and overall evaluations of theme parks through text mining and logistic regression and to verify their internal relationships. Findings Study 1 confirms the impact of six features of theme parks, including food and beverage consumption, merchandising, spatiality, immersive technologies, interactive performances and thematization. Study 2 reveals that finer-grained emotions such as goodness, sadness, disgust, surprise, fear, joy and anger are present in visitor reviews. Study 3 confirms the significant influence of cognitive characteristics and emotions related to theme parks on the overall image through regression analysis. The findings carry meaningful implications for theme park managers, offering guidance on customer needs, perceived negative attributes and how to improve visitor experiences. Originality/value This study explores the attribute characteristics of cognitive and affective images of theme parks and their influence on the overall image, thereby enriching the research on the connotations of cognitive-affective theory. In particular, this study introduces and quantitatively analyses the concept of theme parks for the first time through a large-scale data analysis, which empirically reconciles the contradictions of previous reviews of different definitions of theme parks.

SRF: SpectrumRecombineFormer for Hyperspectral Image Classification

Hyperspectral (HS) imaging is a valuable technique for accurately classifying materials because of the abundance of spectral information and high resolution it provides. However, the characteristics of Hyperspectral images (HSI), such as high-dimensional features and information redundancy, pose significant challenges to data processing. Traditional dimensionality reduction methods often have information loss, high computational complexity, and easy to ignore the strong correlation between HSI spectral bands when dealing with HSI data. Although other methods can achieve satisfactory classification performance, they do not consider the dimensionality reduction of HSI, and they focus on the model performance, which limits further improvement in classification performance. This paper proposes a transformer-based framework called “SpectrumRecombineFormer” (SRF), which is composed of two key modules, namely “Spatial Spectral ReCombination” (SSRC) and “Cross-layer Fusion” (CF). The SSRC is capable of utilizing both adjacent and non-adjacent spectrums to generate the spatial-sequential perceptive representations, which alleviates the effect of the strong correlation between HSI spectral bands. The CF can avoid the loss of information during the feed-forward procedure among layers. Extensive experiments on five existing datasets (widely-adopted Indian Pines, Houston2013, Pavia University, Salinas and KSC) demonstrate the capability of our proposed method to address the above mentioned challenges. Both quantitative and qualitative experimental ablation studies, including visualization results, reveal that the proposed SRF method can successfully and efficiently classify hyperspectral images and surpass the other state-of-the-art methods. For access to the source code, please visit https://github.com/kangpeilun/SRF-HSI-Classification-master .

A Self-Supervised Feature Point Detection Method for ISAR Images of Space Targets

Feature point detection in inverse synthetic aperture radar (ISAR) images of space targets is the foundation for tasks such as analyzing space target motion intent and predicting on-orbit status. Traditional feature point detection methods perform poorly when confronted with the low texture and uneven brightness characteristics of ISAR images. Due to the nonlinear mapping capabilities, neural networks can effectively learn features from ISAR images of space targets, providing new ideas for feature point detection. However, the scarcity of labeled ISAR image data for space targets presents a challenge for research. To address the issue, this paper introduces a self-supervised feature point detection method (SFPD), which can accurately detect the positions of feature points in ISAR images of space targets without true feature point positions during the training process. Firstly, this paper simulates an ISAR primitive dataset and uses it to train the proposed basic feature point detection model. Subsequently, the basic feature point detection model and affine transformation are utilized to label pseudo-ground truth for ISAR images of space targets. Eventually, the labeled ISAR image dataset is used to train SFPD. Therefore, SFPD can be trained without requiring ground truth for the ISAR image dataset. The experiments demonstrate that SFPD has better performance in feature point detection and feature point matching than usual algorithms.

Fine texture Lambertian electromagnetic metasurface generates background blending stealth effect in radar vision

The emergence of diffuse scattering metasurfaces offers the electromagnetic scattering stealth mechanism. However, the background blending stealth effect in radar vision induced by diffuse scattering metasurface has not been specially investigated. Here, a background blending Lambertian diffusive reflective metasurface for imaging radar stealth is designed by electromagnetic dispersion engineering. Compared to a conventional metasurface, the Lambertian metasurface achieves more uniform beam dispersion over a broad frequency range without significant main lobe formation at any frequency point, which provides ways for background fusion stealth. For conceptual validation, a metallic flat plate, coded metasurface, and Lambertian metasurface are fabricated and their radar images are obtained by two-dimensional imaging scanning measurement. As anticipated, the Lambertian metasurface notably alters the imaging edge characteristics in the radar images, effectively concealing the contours of the covered area. This work provides a wideband design method of the coded metasurface, which provides a new idea for designing stealth material and structures from the perspective of radar imaging and is immensely helpful for practical application in jamming recognition stealth of coding metasurfaces.

An automatic cervical cell classification model based on improved DenseNet121

The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network. Firstly, the SE module is embedded in DenseNet121 to increase the model’s focus on the nucleus region, which contains important diagnostic information, and reduce the focus on redundant information. Secondly, the sizes of the convolutional kernel and pooling window of the Stem layer are adjusted to adapt to the characteristics of the cervical cell images, so that the model can extract the local detailed information more effectively. Finally, the Atrous Dense Block (ADB) is constructed, and four ADB modules are integrated into DenseNet121 to enable the model to acquire global and local salient feature information. The accuracy of A2SDNet121 for two and seven-classification tasks on the Herlev dataset is 99.75% and 99.14%, respectively. The accuracy for two, three, and five-classification tasks on the SIPaKMeD dataset reaches 99.55%, 99.75% and 99.22%, respectively. Compared with other state-of-the-art algorithms, the A2SDNet121 model performs better in the multi-classification task of cervical cells, which can significantly improve the accuracy and efficiency of cervical cancer screening.

Classification and prognostic evaluation of ameloblastoma using multiplanar CT imaging: a retrospective analysis

BackgroundAmeloblastoma is the most prevalent odontogenic tumor of the jaw, with a significant recurrence rate. It was conventionally classified radiographically as unilocular or multilocular. As 3D images become more common, there is a need to reassess this classification. This study aimed to conduct a multi-planar assessment of ameloblastoma using CT images, reclassify the lesions based on locularity, and correlate the locularity with imaging characteristics and recurrence.MethodsPanoramic radiographs and CT images of histopathologically confirmed 255 ameloblastoma patients were included. The analysis encompassed imaging features including locularity, expansion, thinning, cortical perforation, and root resorption. Image characteristics associated with recurrence rate were examined, and image features were assessed based on locularity.ResultsOn panoramic radiographs, most ameloblastomas (68.6%) were classified as multilocular, while 31.4% were unilocular. The CT scan demonstrated that 46.3% of the ameloblastomas were pseudo-multilocular, 29.4% were unilocular, and 24.3% were multilocular. Multilocular cases on panoramic radiographs often appeared as pseudo-multilocular on CT. Cortical perforation, root resorption, and size were significantly correlated with CT locularity types (p < 0.05). The recurrence rate was 14.11%, and the risk of recurrence was substantially reduced by mandibulectomy and a larger tumor size. No other variables were significantly related to recurrence.ConclusionAn accurate diagnosis of ameloblastoma and surgical planning to reduce the recurrence rate are contingent upon the use of three-dimensional imaging modalities, such as CT.

DualTransAttNet: A Hybrid Model with a Dual Attention Mechanism for Corn Seed Classification

Varietal purity is a critical quality indicator for seeds, yet various production processes can lead to the mixing of seeds from different varieties. Consequently, seed variety classification is an essential step in seed production. Existing classification algorithms often suffer from limitations such as reliance on single information sources, constrained feature extraction capabilities, time consumption, low accuracy, and the potential to cause irreversible damage to seeds. To address these challenges, this paper proposes a fast and non-destructive classification method for corn seeds, named DualTransAttNet, based on multi-source image information and hybrid feature extraction. High-resolution hyperspectral images of various corn varieties were collected, and a sliding sampling approach was employed to capture feature information across all spectral bands, resulting in the construction of a hyperspectral dataset for corn seed classification. Hyperspectral and RGB image data were then integrated to complement one another’s information and mitigate the insufficient feature diversity caused by single-source data. The proposed method leverages the strengths of convolutional neural networks (CNNs) and transformers to extract both local and global features, effectively capturing spectral and image characteristics. The experimental results demonstrate that the DualTransAttNet model can achieve a compact size of only 1.758 MB and an inference time of 0.019 ms. Compared to typical machine learning and deep learning models, the proposed model exhibits superior performance with an overall accuracy, F1-score, and Kappa coefficient of 90.01%, 88.9%, and 88.4%, respectively. The model’s rapid inference capability and low parameter count make it an excellent technical solution for agricultural automation and intelligent systems, thereby enhancing the efficiency and profitability of agricultural production.

Total variational noise reduction method for EBAPS image based on weighted nuclear norm minimization.

The electron-bombarded active pixel sensor (EBAPS) is a highly sensitive vacuum-solid hybrid low-light imaging device capable of functioning in ultra-low illumination environments as low as 10-4 lx. However, this high sensitivity also causes problems, such as a low signal-to-noise ratio and complex noise. To enhance the quality of low-light night vision images captured by EBAPS and achieve effective imaging in ultra-low illumination, this study proposes a noise reduction algorithm based on the noise characteristics of EBAPS images. By utilizing the weighted nuclear norm minimization (WNNM) as the fundamental framework, several enhanced methods to address the multi-source noise of the EBAPS under ultra-low illumination have been proposed. These methods include outlier removal and variance-stabilizing transformation. To address the lack of edge preservation, a four-directional total variational regularization term has been incorporated into the WNNM model to maintain the image edge. Experimental results demonstrated that the improved method effectively eliminates various types of EBAPS noise while considerably returning the edges, ultimately enhancing the quality of low-light images. This study greatly facilitates subsequent applications of the low-light night vision technology.

Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images

Addressing the common issues of low brightness, poor contrast, and blurred details in images captured under conditions such as night, backlight, and adverse weather, we propose a zero-reference dual-path network based on multi-scale depth curve estimation for low-light image enhancement. Utilizing a no-reference loss function, the enhancement of low-light images is converted into depth curve estimation, with three curves fitted to enhance the dark details of the image: a brightness adjustment curve (LE-curve), a contrast enhancement curve (CE-curve), and a multi-scale feature fusion curve (MF-curve). Initially, we introduce the TCE-L and TCE-C modules to improve image brightness and enhance image contrast, respectively. Subsequently, we design a multi-scale feature fusion (MFF) module that integrates the original and enhanced images at multiple scales in the HSV color space based on the brightness distribution characteristics of low-light images, yielding an optimally enhanced image that avoids overexposure and color distortion. We compare our proposed method against ten other advanced algorithms based on multiple datasets, including LOL, DICM, MEF, NPE, and ExDark, that encompass complex illumination variations. Experimental results demonstrate that the proposed algorithm adapts better to the characteristics of images captured in low-light environments, producing enhanced images with sharp contrast, rich details, and preserved color authenticity, while effectively mitigating the issue of overexposure.

Cross-modal feature interaction network for heterogeneous change detection

ABSTRACT Heterogeneous change detection is a task of considerable practical importance and significant challenge in remote sensing. Heterogeneous change detection involves identifying change areas using remote sensing images obtained from different sensors or imaging conditions. Recently, research has focused on feature space translation methods based on deep learning technology for heterogeneous images. However, these types of methods often lead to the loss of original image information, and the translated features cannot be efficiently compared, further limiting the accuracy of change detection. For these issues, we propose a cross-modal feature interaction network (CMFINet). Specifically, CMFINet introduces a cross-modal interaction module (CMIM), which facilitates the interaction between heterogeneous features through attention exchange. This approach promotes consistent representation of heterogeneous features while preserving image characteristics. Additionally, we design a differential feature extraction module (DFEM) to enhance the extraction of true change features from spatial and channel dimensions, facilitating efficient comparison after feature interaction. Extensive experiments conducted on the California, Toulouse, and Wuhan datasets demonstrate that CMFINet outperforms eight existing methods in identifying change areas in different scenes from multimodal images. Compared to the existing methods applied to the three datasets, CMFINet achieved the highest F1 scores of 83.93%, 75.65%, and 95.42%, and the highest mIoU values of 85.38%, 78.34%, and 94.87%, respectively. The results demonstrate the effectiveness and applicability of CMFINet in heterogeneous change detection.

Improved detection and classification of cryptic ‘Phylloscopus’ warblers with integrated computer vision and deep learning method

The rapid advancement of image recognition technology, especially with the help of YOLO algorithms, has greatly improved the ability to identify and recognize different species. This research focuses on the Phylloscopus burkii cryptic species complex, distinguished by little phenotypic diversity making precise identification challenging. In order to make it easier to distinguish between members of this complex, we suggest an enhanced YOLOv5-based model that employs a part-based approach that keeps part detection and identification classification apart. Our goals included determining whether images taken in natural settings can produce statistically reliable identifications and examining the variation of image characteristics of the species within the burkii complex. A dataset of 289 annotated images was augmented to build 2,890 photographs for training purposes. After training on Amazon Elastic Cloud, the model achieved a mean average precision (mAP) of 94 and a recall of 97.3. The model demonstrated strong precision and recall metrics across species, and the results showed that it worked best at a confidence threshold of 0.50. P. valentini and P. tephrocephalus, in particular, showed considerable overlap, which sometimes resulted in misidentifications, although P. poliogenys and P. whistleri demonstrated excellent accuracy. We recommend training models based on species geographic distributions to improve identification accuracy. This strategy might successfully lower the rate of erroneous identifications, enhancing the caliber of data produced by citizen science projects. In the end, our results support current efforts to conserve biodiversity by offering a solid framework for automating species identification and guiding ecological study. Our goal is to improve species monitoring and data collection precision by utilizing innovative methods based on machine learning.

ChatGPT-4.0 in oral and maxillofacial radiology: prediction of anatomical and pathological conditions from radiographic images.

ChatGPT has the ability to generate human-like text, analyze and understand medical images using natural Language processing (NLP) algorithms. It can generate real-time diagnosis and recognize patterns and learn from previous cases to improve accuracy by combining patient history, symptoms, and image characteristics. It has been used recently for learning about maxillofacial diseases, writing and translating radiology reports, and identifying anatomical landmarks, among other things.

Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks.

This paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80.5 kHz high-frequency square wave signal is input into the battery module and recorded using a high-speed data acquisition card. The signal is processed by the SDP method to generate characteristic images for fault diagnosis. Finally, a deep learning algorithm is used to evaluate the state of the lithium battery. A total of 3000 samples were collected, with 400 samples used for training and 200 for testing for each fault type, achieving an overall identification accuracy of 99.9%, demonstrating the effectiveness of the proposed method.

Filling the Gap: Enhancing Borehole Imaging with Tensor Neural Network

Borehole imaging is crucial in geological research as it offers insights into subsurface formations and supports reservoir assessment, mineral exploration, and hydrocarbon extraction. However, the effectiveness of borehole imaging is limited by the incompleteness of data due to the design constraints of borehole imaging tools. Missing areas in borehole images pose challenges to geologists. While existing methods, such as pattern filling and CNN-based techniques, show some efficacy, they often require a large amount of complete images for training. In recent years, unsupervised deep learning and tensor-based methods have gained attention for their ability to reconstruct missing or degraded geological images by leveraging the structural characteristics of images. In particular, tensor representations based on Tucker decomposition have shown strong capabilities in data completion. Inspired by this, we propose a novel self-supervised Tensor Neural Network (TNN) utilizing Tucker decomposition as our backbone. Since borehole images are wraped to 2D data, converting them into tensor representations is a critical step in leveraging the proposed tensor representation. To achieve this, we introduce the Adaptive Boundary-Detection Cropping with Augmentation algorithm, which adapts 2D images into 3D tensors. After interpolating the tensors using the proposed tensor network, we employ Adaptive Slice Concatenation with Replacement to restore the complete images from the enhanced tensors, ensuring that the tensor representation of 3D data is accurately depicted in the 2D images. The proposed TNN can be further enhanced by incorporating a structural regularizer. Actual data experiments demonstrate that our method effectively fills gaps in borehole images with higher clarity and detail. The completed images retain crucial geological features and textures, surpassing some of the existing self-supervised learning methods.

ENSEMBLE METHOD BASED ON AVERAGING SHAPES OF OBJECTS USING THE PYRAMID METHOD

Context. Image segmentation plays a key role in computer vision. The quality of segmentation is affected by many factors: noise, artifacts, complex shapes of objects. Classical methods cannot always guarantee good success, depending on the quality of the image and the existing noise, they cannot always achieve the desired result. The proposed method uses an ensemble of neural networks, which makes it possible to increase the accuracy and stability of segmentation. Objective. The goal of the work is to develop a new method of combining predictions of neural network ensembles, which can improve segmentation accuracy by combining images of different image sizes. Method. A method is proposed that averages the shapes of objects depicted on prediction masks. A pyramid of images is used to improve segmentation quality, each level of the pyramid corresponds to an increased size of the original image. This approach allows obtaining image characteristics at different levels. For a test image, a prediction is obtained from each neural network in the ensemble, after which a pyramid is built for the image. All pyramid levels are combined into the final image using SAAMC. All obtained final images for each neural network are also combined at the end using SAAMC. The use of an ensemble of neural networks combined with the pyramid method allows for reducing the impact of noise and artifacts on the segmentation results. Results. The use of this method was compared with the usual use of individual neural networks and the ensemble averaging method. The obtained results show that the proposed method outperforms its competitors. Application of the proposed method improved the accuracy and quality of segmentation. Conclusions. The conducted research confirmed the sense of using an ensemble of neural networks and creating a new method of combining predictions. The use of an ensemble of neural networks makes it possible to compensate for the errors and shortcomings of individual neural networks. Using the proposed method can significantly reduce the impact of noise and artifacts on segmentation. Further study and modification of this method will make it possible to further improve the quality of segmentation.

Low-Light Image Enhancement Using CycleGAN-Based Near-Infrared Image Generation and Fusion

Image visibility is often degraded under challenging conditions such as low light, backlighting, and inadequate contrast. To mitigate these issues, techniques like histogram equalization, high dynamic range (HDR) tone mapping and near-infrared (NIR)–visible image fusion are widely employed. However, these methods have inherent drawbacks: histogram equalization frequently causes oversaturation and detail loss, while visible–NIR fusion requires complex and error-prone images. The proposed algorithm of a complementary cycle-consistent generative adversarial network (CycleGAN)-based training with visible and NIR images, leverages CycleGAN to generate fake NIR images by blending the characteristics of visible and NIR images. This approach presents tone compression and preserves fine details, effectively addressing the limitations of traditional methods. Experimental results demonstrate that the proposed method outperforms conventional algorithms, delivering superior quality and detail retention. This advancement holds substantial promise for applications where dependable image visibility is critical, such as autonomous driving and CCTV (Closed-Circuit Television) surveillance systems.

Lossless Image Compression Using Context-Dependent Linear Prediction Based on Mean Absolute Error Minimization.

This paper presents a method for lossless compression of images with fast decoding time and the option to select encoder parameters for individual image characteristics to increase compression efficiency. The data modeling stage was based on linear and nonlinear prediction, which was complemented by a simple block for removing the context-dependent constant component. The prediction was based on the Iterative Reweighted Least Squares (IRLS) method which allowed the minimization of mean absolute error. Two-stage compression was used to encode prediction errors: an adaptive Golomb and a binary arithmetic coding. High compression efficiency was achieved by using an author's context-switching algorithm, which allows several prediction models tailored to the individual characteristics of each image area. In addition, an analysis of the impact of individual encoder parameters on efficiency and encoding time was conducted, and the efficiency of the proposed solution was shown against competing solutions, showing a 9.1% improvement in the bit average of files for the entire test base compared to JPEG-LS.