A novel approach for image compression based on multi-level image thresholding using discrete wavelet transform and cricket algorithm

Now-a-days due to the huge increase in the size of image data Lossy Image Compression is highly used to reduce the image size but without having huge data loss. Image compression using SVD coding algorithm, Compressive Encoders and using prediction Error and Vectorization ratio are proved to have numerous application in image compression. Image compression using SVD coding algorithm involves refactoring of a digital image into three matrixes. Refactoring is achieved by using singular values, and the image is represented with a smaller set of values. Though, encoders cannot directly optimize due to the inherentNon-differentiability of the compression loss but it is out performing recently proposed approaches based on RNNs. The PE-VQ method is based on Prediction Error and Vector Quantisation techniques where image performance is determined using compression ratio and PSNR values using databases namely CLEF med 2009, Corel 1k and standard images like Lena, Barbara etc. Thus, in this research article a comparative study of these three techniquesis carried out where their image quality and compression ratio is examined by using the PSNR values and compression ratios.

The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

At present, screen image compression techniques are mostly designed for computer desktop, and there is still much room for improvement of the image compression quality and compression ratio, etc. when applied to scientific instruments workstation screen image. In order to solve such problems, this paper puts forward a kind of compression method applicable to scientific instruments workstation screen image. Firstly, get the statistics information and analysis of the color value of screen image, and determining its distribution characteristics, and construct a 256-color palette for color quantization through merging the similar color of the window title bar of programs to reduce the number of color types. In addition, use the image layering and improved run-length coding techniques to enhance the continuity of the same color pixels of the background image and the image compression ratio. Test results show that the algorithm applied to scientific instruments workstation screen image has significant advantages in image coding speed, compression ratio and mean square signal to noise ratio of the output image.

Multilevel thresholding is one of the most widely used techniques for image segmentation. A thresholding technique for image segmentation is mainly categorized into two types such as bi-level and multilevel thresholding. A single threshold value is used in bi-level thresholding for image classification such as—foreground object and background object. Bi-level thresholding gives unsatisfactory segmentation results in case of complex image; hence, the idea of multilevel thresholding has been preferred over bi-level thresholding method. In multilevel thresholding, selection of threshold values mostly gives inaccurate values, and it is a time-consuming process. Hence, automatic multilevel thresholding techniques are used as an objective functions to choose optimal threshold values but faces high computational complexity problems. Meta-heuristic algorithms play an important role to reduce the computational complexity of multilevel thresholding. In this paper, we have surveyed various objective functions used in automatic multilevel thresholding and performed a comparative study about the performances of some recent meta-heuristic algorithms, which are widely used in multilevel thresholding. Also, discussed different datasets and metrics used to evaluate multilevel thresholding techniques. In addition, some applications of image segmentation are also discussed.KeywordsParametricNonparametricMeta-heuristicsOptimization

One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.

The digital image segmentation is an open problem that is growing day by day and is attracting the attention of researchers from last few years. Image resolution and their speed has led to the use of thresholding approaches. Image thresholding is simple, easy and effective method for image segmentation. Multi-level image thresholding is a key perspective in several real-time pattern recognition and image processing-based applications. It identifies pixels quickly and effectively in different groups indicating multiple regions in an image. Segmentation of images based on thresholding by using various intelligent optimization techniques with fuzzy entropy is widely utilized for defining thresholds in a better way to use them precisely. In this research, a novel technique for multi-level thresholding is proposed by combining Fuzzy Entropy Type II (FE-TII) with recently developed meta-heuristics named Marine Predators Algorithm (MPA). For achieving optimal thresholds of an image, the maximization of entropy is tedious and consumes a lot of time with an increasing number of thresholds. The MPA method presented is analyzed in context with image segmentation, particularly on thresholds with TII-FE. For this reason, proposed methodology is evaluated using several images along with the distribution of histograms. For analyzing the performance efficiency of the proposed methodology, the results are compared and robustness is tested with efficiency of proposed technique to multi-level image segmentation, several images are used randomly from datasets.

Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu's and Kapur's methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques were introduced for bi-level thresholding. However, these techniques can be expanded to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. To this end, various optimization techniques have been used to overcome this drawback. Recently, a new optimization algorithm called Battle Royal Optimizer (BRO) has been published, which is shown to solve various optimization tasks effectively. In this study, BRO has been applied to yield optimum threshold values in multilevel image thresholding. Here is also demonstrated the effectiveness of BRO for image segmentation on various images from the standard publicly accessible Berkeley segmentation dataset. We compare the performance of BRO to other state-of-the-art optimization-based methods and show that it outperforms them in terms of fitness value, Peak Signal-to-Noise Ratio, Structural Similarity Index Method, Feature Similarity Index Method (FSIM), Color FSIM (FSIMc), and Standard Deviation. These results underscore the potential of BRO as a promising solution for image segmentation tasks, particularly through its effective implementation of multilevel thresholding.

Multi-level image thresholding is a common approach to image segmentation for which population-based metaheuristic algorithms present an interesting alternative to conventional methods that are based on a exhaustive search. In this paper, we propose a novel multi-level image thresholding algorithm based on the Self-Organizing Migrating Algorithm (SOMA), in particular SOMA Team To Team Adaptive (SOMA T3A), a recent variant of SOMA, and an entropy-based fitness function. We evaluate our algorithm on a set of benchmark images on high-dimensional search spaces and with regards to fitness function value and peak signal-to-noise ratio (PSNR). Experimental results demonstrate excellent thresholding performance and our algorithm to outperform nine other state-of-the-art metaheuristics.

Compression of images process is a step in the image processing. It is concerned with the transmission and storage of digitally created images. Fractal coding is a potential image and video compression approach with good reconstruction fidelity and relatively large compression ratios, because of its simplicity and great performance; fractal image compression (FIC) is a particularly popular approach in image compression applications. However, it has a significant disadvantage in the form of a long encoding time. This is because encoding any small bit necessitates a massive similarity search in the original data As a result; the FIC search time is reduced while the quality of the reconstructed images is maintained acceptable level in many introduced paper and other still a study topic in progress. Fractal images are images that are self-similar in that each individual part is the same as the total. This paper will discusses many attempts for more author that working on image and video compression using fractal compression technique based on various approach and with each discuss focuses on the main parameter of compression such compression ratio (CR), peak signal to noise ratio (PSNR) and encoding time, as well as the details of data set the used for testing also writing with each technique to creating fractal video and image compression. Index Terms— FIC, Video compression, parallel processing, Iteration Function System (IFS), Image processing.

Focused on the image data storage, compression and transmission of mars rover, system design and key hardware/software architecture design are carried out in this paper based on the analysis of function requirements and key design features. Image compression algorithm and transmission protocol are proposed, and the image storage and compression module in Mars rover is designed and implemented. The multi-camera partition parallel image file system based on FPGA（Field Programmable Gate Array）is realized. The adaptive first 1 run length coding algorithm for image compression based on DSP is realized on this module. Finally the function of multi-camera image data storage and management, flexible control of image compression ratio, quality progressive transmission, coding of region of interest, anti-error code diffusion and image thumbnail downward transmission are realized in the engineering development, meeting the reliable, efficient and flexible application requirements of China’s first Mars exploration mission.

A BPG (better portable graphics) coder is a novel approach that aims to replace common standards of compression such as JPEG, JPEG2000 and so on. That is why, the BPG coder needs a detailed analysis of its basic characteristics from the viewpoint of visual quality and compression ratio. The BPG coder can use different modes of chroma subsampling for color and three-channel images and it is worth analyzing and comparing them. In practice, images to be compressed are often noisy. Then, lossy compression of such images has a specific noise filtering effect. In particular, optimal operation point (OOP) might exist where compressed image quality is closer to the corresponding noise-free (true) image than uncompressed (original, noisy) image quality according to certain criterion (metric). It is also needed to analyze the coder performance from compression ratio point of view. In this paper, we pay attention on impact of different chroma subsampling modes on image quality and compression ratio. Based on simulation results obtained for a set of color images, the best possible ways of compression are recommended.

We present an image compression technique using a feedforward neural network. The neural network has three layers: one input layer, one hidden layer and one output layer. The inputs of the neural network are original image data, while the outputs are reconstructed image data which are close to the inputs. If the amount of data required to store the hidden unit values and the connection weights to the output layer of the neural network is less than the original data, compression is achieved. In our experiments, we achieved a compression ratio of about 10 with good reconstructed image quality. The neural network construction algorithm we used has an attractive feature that each addition of a hidden unit to the network will always improve the image quality. Thus our compression scheme is flexible in the sense that the user can trade between image quality and compression ratio depending on the application requirements.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The article describes the current state of data transfer protocols and methods of image and video compression through the use of artificial neural networks, namely convolutional multilayer networks and deep structured learning. Based on recent publications, a comparative analysis of the performance of classical compression methods and methods based on neural networks was performed. The most effective are those compression methods which are based on decorrelation transforms, namely discrete cosine (JPEG standard) and wavelet (JPEG-2000 standard) transforms. The transform coefficients have a well-understood physical content of spatial frequencies and can be further quantized for a more optimal representation of components that are less important for human perception. The HEVC standard guarantees a more efficient image compression scheme that further takes advantage of the similarity of adjacent blocks and uses interpolation (intracoding). Based on the HEVC standard, the BPG (better portable graphics) format was developed to be used on the Internet as an alternative to JPEG, which is much more efficient than other standards. An overview of the current state of open standards, provided in the article, gives an explanation of what properties of neural networks can be applied to image compression. There are two approaches towards the compression using neural networks: in case of the first approach neural network is used as a part of an existing algorithm (hybrid coding), and in case of the second approach the neural network gives a concise representation of the data (compression network). The final conclusions were made as regards to the application of these algorithms in H.265 protocol (HEVC) and the possibility of creating a new protocol which is completely based on the neural network. Protocols using neural network show better results during image compression, but are currently hard to be subjected to standardization in order to obtain the expected result in case of different network architects. We may expect and predict an increase in the need for video transmission in the future, which will bump into the imitating nature of classical approaches. At the same time, the development of specialized processors for parallel data processing and implementation of neural networks is currently underway. These two factors indicate that neural networks must be embedded into the industrial data standards.

This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

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