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The assurance of secret data transmitted over the Internet and confining its access to explicit grouped information has become a significant security and protection issue. To hide the presence of such information, a robust and blind color image steganography algorithm, using fractal cover images, Singular Value Decomposition (SVD), Integer Wavelet Transform (IWT), and Discrete Wavelet Transform (DWT) is proposed in this paper. The proposed algorithm conceals the color secret image intelligently inside a fractal cover image thus creating a unique stego-image that is virtually a replica of the original cover image. The novelty of this proposed blind image hiding method lies in the fact that a small file size of a unique fractal image can be used to conceal a large amount of information payload. The fractal cover images can be implemented without debasing its quality to make the shrouded information sufficiently strong against image processing attacks. The algorithm possesses features like robustness, imperceptibility, security, and greater hiding capacity due to the application of fractal compression, DWT-SVD, and IWT-SVD hybrid transform schemes. The secret image is inserted in U and V components of the fractal cover with a scaling factor based on the HVS model and a stego-image with sufficiently large keyspace is created. Furthermore, the proposed work compares the performance analysis and security offered by IWT- SVD and DWT- SVD hybrid combinations for different fractal covers using certain metrics like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), SSIM (Structural Similarity Index), BRISQUE (Blind/ Reference less Image Spatial Quality Evaluator) score and Bits per pixel (BPP). The results prove that image degradation is very less (less than 0.6%) in the proposed algorithm even when the secret image file size is more than double the file size of the fractal cover image which also saves the bandwidth and storage requirements.

Image compression research has increased dramatically as a result of the growing demands for image transmission in computer and mobile environments. It is needed especially for reduced storage and efficient image transmission and used to reduce the bits necessary to represent a picture digitally while preserving its original quality. Fractal encoding is an advanced technique of image compression. It is based on the image's forms as well as the generation of repetitive blocks via mathematical conversions. Because of resources needed to compress large data volumes, enormous programming time is needed, therefore Fractal Image Compression's main disadvantage is a very high encoding time where decoding times are extremely fast. An artificial intelligence technique similar to a neural network is used to reduce the search space and encoding time for images by employing a neural network algorithm known as the “back propagation” neural network algorithm. Initially, the image is divided into fixed-size and domains. For each range block its most matched domain is selected, its range index is produced and best matched domains index is the expert system's input, which reduces matching domain blocks in sets of results. This leads in the training of the neural network. This trained network is now used to compress other images which give encoding a lot less time. During the decoding phase, any random original image, converging after some changes to the Fractal image, reciprocates the transformation parameters. The quality of this FIC is indeed demonstrated by the simulation findings. This paper explores a unique neural network FIC that is capable of increasing neural network speed and image quality simultaneously.

Fractal image compression is one of the efficient structure-based methods in applications where images are compressed only once but decoded several times due to its resolution-independent feature and fast reconstruction time. However, it has high computational complexity restricting practical use most of the time. Although several methods have been developed to speed up the compression process, these do not satisfy the compression time or the decoded image quality requirements. The affine transforms of image blocks used in fractal coding require a huge number of multiplications and additions and are very expensive in computation that may also slow down the compression process. This paper presents a novel fractal image compression using a fast affine transform and hierarchical classification scheme. The applied affine transform computation algorithm of image blocks uses relationships among neighboring pixels of transformed image block that significantly reduces the number of multiplication and addition operations. Then, this strategy with hierarchical classification and class-wise domain sorting is applied in fractal coding with quad-tree and horizontal vertical partitioning schemes to reduce compression time. Experimental results show that the quad-tree-based fractal coding with the proposed scheme can significantly speed up the compression process keeping image quality and compression ratio almost unchanged.

Over the last few years, the computer vision has a huge impact in the field of information technology. The foundation for computer vision is laid by image processing. Digital Image Processing is one of the vast area of research; with three indispensable aspects as; Image De-noising, Image Compression & Image Security; with multiple applications in the field of automotive, medical, space, security systems, military & many more. This survey paper includes the evolutionary analysis of various image de-nosing techniques such as Median filtering, Pixel similarity weighted frame averaging, Gaussian filtering, Bilateral filtering, Anisotropic diffusion; that are used for betterment of system performance in terms of noise suppression, edge preservation, texture detection, Operating frequency, PSNR & many more. Moreover, the paper also includes the evolutionary analysis of multiple image compression techniques based on DCT, DWT, fractal image compression & neural network. Finally, the various image security techniques are analyzed. From the study of various literatures on image processing, it is found that combine model of image processing with all the three stages including image de-noising followed by image compression & image security is not yet discussed.

During the intensive development of information systems, the volumes of data necessary for storing and processing information, in particular graphic images, grow. One of the reasons for this phenomenon is the desire to continually improve the quality of the content we receive. The ever-growing demand for image quality requires the development of new and improvement of existing approaches to information compression. Compression algorithms use the presence of so-called surplus in the data, which can be eliminated during data storage and restored during playback. Currently in demand methods are based on storing only low-frequency components. Such methods are used in JPEG, MPEG compression algorithms. The disadvantage of such algorithms is not a large compression ratio. In this regard, methods have emerged based on fractal data compression. The main idea of ​​the method is to store the image as affine transformations, which leads to its compression. The paper proposes preliminary processing of graphic data for storing them in the form of a file with the * .json extension. The application of a further fractal compression algorithm to the already obtained file allows to reduce the processing time of data, computational calculations. The resulting file will have the advantages of fractal compression such as decompression speed, better compression ratio and higher resolution compared to * .jpeg and * .bmp. Despite all the advantages, fractal compression of graphic information is used quite rarely. This is due to the complexity of the algorithm, the lack of a sufficient number of specialists on this issue and the estimate of the licensed software. The improvement is aimed at simplifying the algorithm and its implementation will avoid these drawbacks and expand the scope of fractal compression.

Applications of image compression have been gaining importance over the years in medical fields. The medical imaging modalities produced a large amount of information on all levels of hospital care. This information, in the form of images, needs to be stored for future references. While compressing the medical images, it is necessary to maintain high diagnostic quality with a high compression rate. Several image compression approaches have been developed towards the direction of the storage space problem in such a way that the doctors accurately and reliably diagnose the patient’s diseases from the reconstructed image after decompression. A high compression ratio is required to reduce the storage space due to the large size of medical images. Fractal image compression is a lossy technique to compress the image in a coded form instead of pixels and is differentiated by its long encoding time with a high compression ratio, resolution-independent, fast decoding, and self-similarity. The main purpose of this paper is to present a comparative performance study of the three coding schemes of fractal compression for grayscale medical images based on fixed partition. The first two coding schemes are based on the pixel-pattern measure and the third scheme is a proposed method based on the fractal dimension for complexity measure of range and domain blocks. The comparative study included encoding time, peak signal to noise ratio, and compression ratio, as a result, has been accomplished.

Fractal Image Compression is a lossy compression method for images based on fractals rather than pixels, which are best suited for texture and natural images. It works on self similarity property, relying on the fact that parts of an image often resemble other parts of the same image. It takes long encoding time and affects the image quality. This paper introduces an improved quantized quad tree technique for fractal image compression. Quantized quad tree method divides the original gray level image into various blocks depending on features presented in image. Simulation results show that with the quantized quad trees compression improves the compression ratio and PSNR without losing the quality of the image compression with range block, domain block and iterations technique.

This study presents insights into the computational complexity of fractal image compression (FIC) algorithms. Unlike JPEG, a fractal encoder necessitates more CPU time in contrast to the decoder. The study examines various factors that impact the encoder and its computational cost. Many researchers have dedicated themselves to the field of fractal encoding to overcome the computational cost of the FIC algorithm. Here, this study offers a look over the approaches in the aspect of time complexity. The automated baseline fractal compression algorithm is studied to demonstrate the understanding of delay in the encoder. The study establishes how various approaches trade-off between the quality of decoder, compression ratio, and CPU time. The experiment section shows the bargain between fidelity criteria of the baseline algorithm.

Fractal image compression depends on representing an image using affine transformations. The main concern for researches in the discipline of fractal image compression (FIC) algorithm is to decrease encoding time needed to compress image data. The basic technique is that each portion of the image is similar to other portions of the same image. In this process, there are many models that were developed. The presence of fractals was initially noticed and handled using Iterated Function System (IFS); that is used for encoding images. In this paper, a review of fractal image compression is discussed with its variants along with other techniques. A summarized review of contributions is achieved to determine the fulfillment of fractal image compression, specifically for the block indexing methods based on the moment descriptor. Block indexing method depends on classifying the domain and range blocks using moments to generate an invariant descriptor that reduces the long encoding time. A comparison is performed between the blocked indexing technology and other fractal image techniques to determine the importance of block indexing in saving encoding time and achieving better compression ratio while maintaining image quality on Lena image.

The fractal image compression (FIC) algorithm is difficult to be widely used in real-time applications due to the huge consumption of its encoding time. Inspired by the fact that in the decoding process, for any original image, a fixed point is generated by the iterations of the fractal codes, the authors propose a specific update search FIC (SUSFIC) algorithm, which uses a scale number to control the update times of the fractal codes and to find acceptable matching domain blocks rather than the best ones in the selected domain blocks pool. To further reduce the computation time, in their proposed algorithm, the image blocks created by the equidistant sampling in the range of the original blocks are used to replace themselves when calculating the correlation coefficients as the distances between the adjacent domain blocks. The experimental results presented show that their proposed SUSFIC algorithm has a significant improvement in encoding time under the premise of setting an appropriate search update threshold and maintaining image quality when compared with the state-of-the-art FIC algorithms. Therefore, it is a better FIC algorithm .

Image Compression techniques have become a very important subject with the rapid growth of multimedia application. The main motivations behind the image compression are for the efficient and lossless transmission as well as for storage of digital data. Image Compression techniques are of two types; Lossless and Lossy compression techniques. Lossy compression techniques are applied for the natural images as minor loss of the data are acceptable. Entropy encoding is the lossless compression scheme that is independent with particular features of the media as it has its own unique codes and symbols. Huffman coding is an entropy coding approach for efficient transmission of data. This paper highlights the fractal image compression method based on the fractal features and searching and finding the best replacement blocks for the original image. Canonical Huffman coding which provides good fractal compression than arithmetic coding is used in this paper. The result obtained depicts that Canonical Huffman coding based fractal compression technique increases the speed of the compression and has better PNSR as well as better compression ratio than standard Huffman coding.

There are some problems in the visualization of 3D model of buildings at present, such as redundant texture data and large memory occupation of loading texture data, which pose challenges to the smooth loading of 3D building visualization. In this paper, we propose a method which using fractal quadtrees to simplify texture data and compress management. Firstly, this method uses the fractal self-similarity and the feature that the same texture has the same fractal dimension to screen all the textures, when the fractal dimension of multiple textures is within the threshold range. Radon transform will be performed on these textures to calculate their standard deviation and further simplify the texture. Secondly, we use fractal texture compression to create a multi-resolution texture data structure with quadtree structure, so that the building 3D model can achieve dynamic visualization of different scales. Finally, the parallel texture mapping of all faces of the 3D model of the building is implemented according to the texture calling rules. Experimental results demonstrate that the texture is managed by the method proposed in this paper, which shortens the loading time of texture and reduces the memory occupation by about 48.92%. Therefore, the method of texture simplification and compression proposed in this paper has a significant effect in the process of building 3D model visualization.

Satellite images comprise very complex spatial features that challenge the traditional image processing and classification techniques. In the last decade, fractal compression of satellite images had emerged to ease and improve the classification strength of the machine learning classifiers. Image compression has become the most essential step to deal with the high-dimensional data produced by remote sensing applications. The paper represents the survey study focussing on analyzing the effect of fractal features on satellite image classification using various machine learning techniques. In the process, the recently published research papers, conference articles, authenticated writings published in Google Scholar, Elsevier, Springer, and other websites have been considered. The work also summarised the generalized steps followed by various literature cited satellite image classification works. The study also summarises the best classifier among the group of classifiers used by researchers to guide future researchers. The comparative analysis based on the class predicted using different classifiers had shown that with time fractal-based satellite image classification had gained much popularity over the past decade.

In the field of Image compression and particularly for the process of image encoding, Fractal image compression (FIC) technique plays a vital role. This technique is based on fractals present in an image and also it is capable of generating the copying blocks dependent on numerical changes. The only drawback in the Fractal image compression is that the time taken during the encoding process when the data is large. Thus, it is necessary to optimize the encoding process for efficient resource utilization as optimization algorithms are generally known for converging behavior. This research work attempts to apply the Tree Seed Algorithm as a characteristic enlivened high-level algorithm to improve FIC. Output shows the improvement of such calculation under various types (encoding time, pressure proportion, top sign to clamor proportion, and mean square error. Moreover, an examination with a portion of the current techniques underlines this addition.

Intelligent healthcare systems need recording, sending and treating large amounts of medical data produced from various kinds of medical sensors nodes. This is a challenging task and may lead to increase the network traffic, latency, and energy consumption. Therefore, it is important to apply a health data reduction mechanism for eliminating the data redundancy and saving the energy thus enhance the smart healthcare system performance. In this paper, a potential EEG Fractals compression model is proposed for reducing the transmitted EEG traffic from Patient Data Aggregator (PDA) to the destination (doctor, smart hospitals, emergency response, etc.). The proposed model supports EEG patient data communication and improve Wireless Body Sensor Network by reducing network traffic. Main model metrics are inspected as the determined Fractals Block Size found to be the vital playing role on producing higher Compression Ratio (CR) and drive the required Percentage Residual Difference (PRD). Both results and performance are compared with other techniques as the proposed model has outperformed them completely. The resultant CR can reach up to 160 and keep PRD less than 1.

Fractal image compression (FIC) is a very popular technique in image compression applications due to its simplicity and superior performance. However, it has a major drawback, which is the long encoding time. This is due to the requirement of performing huge similarity search for encoding each small portion in the image. Thus, reducing the search time of FIC while keeping the quality of reconstructed images at acceptable level is still an active research topic. Therefore, this paper has focused on studying the search problem of the conventional full-search FIC algorithm and the impact of employing a spatial dynamic search technique instead with the matching threshold strategy. Unlike the conventional full-search method that is a spatially static where the search starts from a fixed position (normally from the top-left corner of the image to the bottom-right corner) regardless of the position of the range block being encoding, the idea of the dynamic search method is simple, but effective, and it is based on starting the search from the closest domain block to the range block that needs to be encoded. These two search schemes are tested under different matching threshold values, in which the search is terminated whenever a domain block with an acceptable matching level is found. To make the study comprehensive, the test is performed for different image sizes and types, range block and partitioning step sizes, and quantization levels. The experimental results show the significant impact of using the dynamic search method instead of the conventional search method specifically when the threshold is large. For the best encoding parameters, the improvement amount that can be achieved is near to 90 % in terms of search reduction and 1.6 dB PSNR in terms of image quality.

Partial discharge (PD) measurement is used for a diagnosis and performance assessment of the solid insulation material inside the high-voltage (HV) equipment. PD measurement indicates the presence of voids, cracks and imperfection present in solid insulation material. The major problem associated with this measured PD signal is heavily contaminated by noise which results in reduction in PD pattern recognition. The objective of this work is to measure and de-noise the PD signal due to cavity and recognize two different size of cavities present in three different locations, namely near HV electrode, center and lower electrode. In first part, the measured PD signal is de-noised using translation invariant wavelet transform. In second part, the three-dimensional (φ–q–n) PD patterns are extracted from the de-noised PD data. Then, it is subjected to canny edge detection technique, and the features like horizontal and vertical fractal dimension averages are evaluated using fractal image compression-based semi-variance technique. For classification, multi-class nonlinear support vector machine has been proposed to classify position and size of the cavity based on the PD fingerprints. The findings of this proposed work can be used to design a solid basis for an recognition of cavity size and position in an electrical apparatus.

Fractal image compression coding algorithm is a novel image compression technology; however, the long encoding time and unacceptable image reconstruction quality remain the primary obstacles in practical application. The purpose of this study is to improve the coding quality from the perspective of grey level transform and feature extraction. In this study, a novel orthogonal sparse fractal coding algorithm based on image texture feature is proposed. The authors define a normalised version as the new grey description feature of the image block so that two improved methods are scientifically combined in theory and algorithm. First, orthogonal sparse grey level transform based on sparse decomposition improves image reconstruction quality and decoding speed. Then, the similarity measure matrix, which stores the variance feature between range blocks and domain blocks, is used to reduce redundancies and encoding time. Simulation results show that the proposed algorithm in this study can obtain better image reconstruction quality and speed up encoding time significantly as compared to the conventional fractal coding schemes.

Fractal coding is a lossy image compression technique, which encodes the image in a way that would require less storage space using the self-similar nature of the image. The main drawback of fractal compression is the high encoding time. This is due to the hard task of finding all fractals during the partition step and the search for the best match of fractals. Lately, GPUs (Graphical Processing Unit) have been exploited to implement fractal image compression algorithms due to their high computational power. The prime aim of this paper is to design and implement a parallel version of the Fisher classification scheme using CUDA to exploit the computational power available in the GPUs. Fisher classification scheme is used to reduce the encoding time of fractal images by limiting the search for the best match of fractals. Encoding time, compression ratio and peak signal-to-noise ratio was used as metrics to assess the correctness and the performance of the developed algorithm. Eight images with different sizes (512 × 512, 1024 × 1024 and 2048 × 2048) have been used for the experiments. The conducted experiments showed that a speedup of 6.4 × was achieved in some images using NVIDIA GeForce GT 660 M GPU.

Encoding complexity reduction is an active area of research in the field of fractal image compression. In this proposed method, affine transformation is predicted to reduce the computational complexity and score value is calculated to further minimise the number of computations in the exhaustive process of encoding. In this hybrid wavelet- fractal image compression, relative geometric transformation of range and domain blocks is determined using a reference matrix as GR and GD respectively. Then affine transformation is predicted as the geometric composition of GR and GD, using Cayley table. Score value is defined individually for domain and range blocks. It is the absolute value of maximum change in the intensity in the respective block. An important merit of this proposed method is that the encoding time is highly reduced in comparison to other recent fractal coding techniques, with a better value of PSNR. This method is 641 times faster than Standard fractal image compression method (SFIC). Cayley table is utilized for the first time in fractal image compression. This lossy compression technique can be used in multimedia applications to handle the situation of limited storage space and bandwidth and also to transmit enormous amount of data with acceptable value of PSNR. As the medical images are huge in size a good lossy compression technique is required to store them in medical archives in an economical manner. Since this proposed method provides a good trade-off between compression ratio and the quality of reconstructed image, it is a better solution for the storage of medical images in an efficient manner.

Image Compression is very active research area specializing on how to compression and decompression of images. The various methods have been proposed for fractal image compression techniques for storage and drawbacks. The complexity in process will affect performance of the existing system to make insufficient. In this paper, the proposed research work presented a block based feature compression of image in the generation of feature sets. The feature sets are generated based such as vertical, horizontal and are extracted from the image based on range and domain blocks. The extracted features are carried out with encoding and decoding the images. The experimental result shows that block based feature method provides better compression ratio and other parameters when compared with the existing methods such as BTC, Wavelet and DCT.