Range Block Research Articles

Gradient based fractal image compression using Cayley table

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 Super Resolution Using Fractal Coding and Residual Network

Fractal coding techniques are an effective tool for describing image textures. Considering the shortcomings of the existing image super‐resolution (SR) method, the large‐scale factor reconstruction performance is poor and the texture details are incomplete. In this paper, we propose an SR method based on error compensation and fractal coding. First, quadtree coding is performed on the image, and the similarity between the range block and the domain block is established to determine the fractal code. Then, through this similarity relationship, the attractor is reconstructed by super‐resolution fractal decoding to obtain an interpolated image. Finally, the fractal error of the fractal code is estimated by the depth residual network, and the estimated version of the error image is added as an error compensation term to the interpolation image to obtain the final reconstructed image. The network structure is jointly trained by a deep network and a shallow network. Residual learning is introduced to greatly improve the convergence speed and reconstruction accuracy of the network. Experiments with other state‐of‐the‐art methods on the benchmark datasets Set5, Set14, B100, and Urban100 show that our algorithm achieves competitive performance quantitatively and qualitatively, with subtle edges and vivid textures. Large‐scale factor images can also be reconstructed better.

Research on fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming

Regarding the problems of insufficient image segmentation intelligence, low compression rate, slow speed for global searching to find the optimal fractal image compression encoding, and bad decoding effect, this article proposes the fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming. Firstly, according to the accurate and fast image classification of deep convolutional neural network and the fast search and matching encoding advantages of gene expression programming, it realizes theoretically the action mechanism of fractal image compression hybrid encoding by combining the convolutional neural network and the gene expression programming; then, it uses the deep convolutional neural network to train and classify the image, and uses the adaptive quadtree segmentation method to segment the classified image, thus generating the domain block and range block classification set. According to the action mechanism of gene expression programming in fractal image compression encoding, it then quickly obtains the optimal solution of fractal image compression encoding by searching and encoding the sub-blocks of range block classification set and the classification set corresponding to the domain. Finally, in the CPU/GPU environment, it conducts the comparative experiment with basic fractal image compression algorithm and fractal image compression algorithm based on convolutional neural network. The experimental results show that this proposed algorithm outperforms similar algorithms in terms of image segmentation speed and accuracy as well as fractal compression encoding speed and compression ratio. Therefore, this algorithm is a fractal image compression algorithm with intelligent segmentation, fast encoding and high compression ratio.

Characterization of morphology in ring-opening metathesis polymerized novel solid block copolymer electrolytes by atomic force microscopy and X-ray scattering

Block copolymers present the ability to design solid polymer electrolytes to include both ion conductivity and structural features which may lead to improved safety in lithium ion batteries. We report a morphology study of novel block copolymer electrolytes that were synthesized using ring opening metathesis polymerization. The monomers have an oxanorbornene dicarboximide backbone where the first block has oligomeric (n = 12) ethylene oxide (OEO) side chains and the second block has phenyl side groups. The former block achieves high salt solubility, while the latter block is a structural component with a high glass transition temperature. Block copolymers have been synthesized covering a range of compositions from 38 to 70 wt % of the phenyl containing block, and have been studied neat and with bis(trifluoromethane)sulfonimide lithium salt. The resulting morphologies were investigated using atomic force microscopy and small angle X-ray scattering (SAXS). Solvent vapor annealing was found to enhance ordering in the neat copolymer thin films and the addition of salt with solvent vapor annealing further increased long range order. Cylinder and lamellar structures dominate the observed morphologies and the addition of salt increases ordering and the range block copolymer compositions with lamellar structure. SAXS results demonstrate modest ordering, reinforce the observations from AFM, and show an increase in domain size with an increase in salt concentration.

The effect of ambient temperature on electric power generation in natural gas combined cycle power plant—A case study

Natural gas combined cycle power plants (CCPPs) are widely used to meet peak loads in electric energy production. Continuous monitoring of the output electrical power of CCPPs is a requirement for power performance. In this study, the role of ambient temperature change having the greatest effect on electric production is experimentally investigated for a natural gas CCPP. The plant has generated electricity for fourteen years and setup at 240 MW in Aliağa, İzmir, Turkey. Depending on the seasonal temperature changes, the study data were obtained from each gas turbine (GT), steam turbine (ST) and combined cycle blocks (CCBs) in the ambient temperature range of 8–23 °C. In electric energy production, an important decrease was in the GTs because of the temperature increase. This decrease indirectly affected the electric energy production of the STs. As a result, the efficiency of each GT, ST and CCB decreased, although the quantity of fuel consumed by the controllers in the plant was reduced. As a result of this data, it has been recommended and applied that additional precautions have been taken in the power plant to bring the air entering the combustion chamber to ideal conditions and necessary air cooling systems have been installed.

An Autofocus Cartesian Factorized Backprojection Algorithm for Spotlight Synthetic Aperture Radar Imaging

A backprojection (BP) algorithm is recognized as an ideal method for high-resolution synthetic aperture radar (SAR) imaging. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm is proposed recently to avoid massive interpolations and improve the performance. However, integrating autofocus techniques with the CFBP has not been discussed. In this letter, an autofocus CFBP algorithm is proposed to compatibly combine the autofocus processing within the CFBP. After modifying the spectrum compression step in the CFBP, the approximate Fourier transformation (FT) relationship between the modified compensated subaperture images and the corresponding range-compressed phase history data in the Cartesian coordinate is revealed. The phase error is obtained by the multiple aperture map drift method, and the singular value decomposition total least square method is combined to improve the estimate robustness. Employing the range blocking method, the range variance of the phase error is compensated. The proposed algorithm inherits the advantages of the CFBP. Experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the proposed approaches.

Fractal Image Compression Based on High Entropy Values Technique

There are many attempts tried to improve the encoding stage of FIC because it consumed time. These attempts worked by reducing size of the search pool for pair range-domain matching but most of them led to get a bad quality, or a lower compression ratio of reconstructed image. This paper aims to present a method to improve performance of the full search algorithm by combining FIC (lossy compression) and another lossless technique (in this case entropy coding is used). The entropy technique will reduce size of the domain pool (i. e., number of domain blocks) based on the entropy value of each range block and domain block and then comparing the results of full search algorithm and proposed algorithm based on entropy technique to see each of which give best results (such as reduced the encoding time with acceptable values in both compression quali-ty parameters which are C. R (Compression Ratio) and PSNR (Image Quality). The experimental results of the proposed algorithm proven that using the proposed entropy technique reduces the encoding time while keeping compression rates and reconstruction image quality good as soon as possible.

A Review of Image Compression Using Fractal Image Compression with Neural Network

Generally the fractal image compression is a new process in the images compression. It is a block based image compression technique, which detects and decodes the existing similarities between different regions in the image. The main disadvantage of FIC is that the encoding time is comparatively very high, w here as the decoding time is very short. An artificial intelligence technique like neural network is used to reduce the search space and encoding time for the MRI images with an algorithm called as “back propagation” neural network algorithm. Initially, MRI image is divided into ranges and domains of fixed size. The best matched domain is selected for each range block and its range index and best matched domain index are produced, which acts as input to the expert system and which results reduced the sets of matched domain blocks. The neural network is then trained with these resultant values. This trained net is now used to compress other MRI images which lead to a very less encoding time. During the decoding phase, the transformation parameters are recursively applied to any random original image, which then converges to the fractal image after some changes. The simulation results show that the performance of this Neural Network based FIC is really. This paper shows the neural network based FIC which produces high development in encoding time without corrupting the image quality when compared to normal FIC.

A Review of Image Compression Using Fractal Image Compression with Neural Network

Generally the fractal image compression is a new process in the images compression. It is a block based image compression technique, which detects and decodes the existing similarities between different regions in the image. The main disadvantage of FIC is that the encoding time is comparatively very high, w here as the decoding time is very short. An artificial intelligence technique like neural network is used to reduce the search space and encoding time for the MRI images with an algorithm called as “back propagation” neural network algorithm. Initially, MRI image is divided into ranges and domains of fixed size. The best matched domain is selected for each range block and its range index and best matched domain index are produced, which acts as input to the expert system and which results reduced the sets of matched domain blocks. The neural network is then trained with these resultant values. This trained net is now used to compress other MRI images which lead to a very less encoding time. During the decoding phase, the transformation parameters are recursively applied to any random original image, which then converges to the fractal image after some changes. The simulation results show that the performance of this Neural Network based FIC is really. This paper shows the neural network based FIC which produces high development in encoding time without corrupting the image quality when compared to normal FIC.

The Use of Quadtree Range Domain Partitioning with Fast Double Moment Descriptors to Enhance FIC of Colored Image

In this paper, an enhanced fractal image compression system (FIC) is proposed; it is based on using both symmetry prediction and blocks indexing to speed up the blocks matching process. The proposed FIC uses quad tree as variable range block partitioning mechanism. two criteria’s for guiding the partitioning decision are used: The first one uses sobel-based edge magnitude, whereas the second uses the contrast of block. A new set of moment descriptors are introduced, they differ from the previously used descriptors by their ability to emphasize the weights of different parts of each block. The effectiveness of all possible combinations of double moments descriptors has been investigated. Furthermore, a fast computation mechanism is introduced to compute the moments attended to improve the overall computation cost. the results of applied tests on the system for the cases “variable and fixed range” block partitioning mechanism indicated that the variable partitioning scheme can produce better results than fixed partitioning one (that is, 4 × 4 block) in term of compression ratio, faster than and PSNR does not significantly decreased.

Underwater Acoustic Image Encoding Based on Interest Region and Correlation Coefficient

It is difficult for the conventional image compression method to achieve good compression effect in the underwater acoustic image (UWAI), because the UWAI has large amount of noise and low correlation between pixel points. In this paper, fractal coding is introduced into UWAI compression, and a fractal coding algorithm based on interest region is proposed according to the importance of different regions in the image. The application problems of traditional quadtree segmentation in UWAIs was solved by the range block segmentation method in the coding process which segmented the interest region into small size and the noninterest region into large size and balanced the compression ratio and the decoded image quality. This paper applies the classification, reduction codebook, and correlation coefficient matching strategy to narrow the search range of the range block in order to solve the problem of the long encoding time and the calculation amount of encoding process is greatly reduced. The experimental results show that the proposed algorithm improves the compression ratio and encoding speed while ensuring the image quality of important regions in the UWAI.

Parallel Fractal Compression Method for Big Video Data

With the development of technologies such as multimedia technology and information technology, a great deal of video data is generated every day. However, storing and transmitting big video data requires a large quantity of storage space and network bandwidth because of its large scale. Therefore, the compression method of big video data has become a challenging research topic at present. Performance of existing content‐based video sequence compression method is difficult to be effectively improved. Therefore, in this paper, we present a fractal‐based parallel compression method without content for big video data. First of all, in order to reduce computational complexity, a video sequence is divided into several fragments according to the spatial and temporal similarity. Secondly, domain and range blocks are classified based on the color similarity feature to reduce computational complexity in each video fragment. Meanwhile, a fractal compression method is deployed in a SIMD parallel environment to reduce compression time and improve the compression ratio. Finally, experimental results show that the proposed method not only improves the quality of the recovered image but also improves the compression speed by compared with existing compression algorithms.

A New Fractal Watermarking Method for Images of Text

A new method, using the orthogonal fractal coding is developed for fractal watermarking of high contrast, low-density images of texts. In this method, image is divided into one pixel height sub-images. Each sub-image is coded separately using the orthogonal fractal coding technique. A binary watermark is re-ordered using a chaotic sequence. The binary watermark is inserted into the range block means of fractal codes. This fractal code is further decoded to obtain the watermarked image. The watermark sequence is retrieved by comparing the original image and the watermarked code. The extracted watermark is re-ordered using the key of the chaotic sequence. The method is robust against JPEG and noise attacks and has a very low watermark visibility.

High-Speed Fractal Image Compression Featuring Deep Data Pipelining Strategy

A new architecture based on deep data pipelining is proposed for implementing fractal compression of high-resolution images in real time. The general idea is to partition an image into overlapping range and domain blocks in which four range blocks constitute one domain block. In this way, two matching operations can be performed simultaneously using two processor units. Further reduction in the encoding time is achieved by exploiting the inherently high degree of correlation among pixels in the neighborhood areas and restricting the search in the neighboring blocks only. The design is synthesized on Altera Stratix IV field-programmable gate array and optimized at circuit level in order to achieve a high-speed implementation. The proposed architecture is evaluated in terms of the peak signal-to-noise ratio (PSNR), the runtime, the memory utilization, and the compression ratio (CR). Experimental results suggest that the proposed architecture is able to encode a 1024 × 1024 size image in 10.8 ms with PSNR and CR averaging at 27 dB and 34:1, respectively. Meanwhile, the energy dissipation is approximately 0.5 W which is comparable to the state-of-the-art fractal processors.

Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging.

This paper presents a modified approach for high-resolution, highly squint synthetic aperture radar (SAR) data processing. Several nonlinear chirp scaling (NLCS) algorithms have been proposed to solve the azimuth variance of the frequency modulation rates that are caused by the linear range walk correction (LRWC). However, the azimuth depth of focusing (ADOF) is not handled well by these algorithms. The generalized nonlinear chirp scaling (GNLCS) algorithm that is proposed in this paper uses the method of series reverse (MSR) to improve the ADOF and focusing precision. It also introduces a high order processing kernel to avoid the range block processing. Simulation results show that the GNLCS algorithm can enlarge the ADOF and focusing precision for high-resolution highly squint SAR data.

A 2-D Space-Variant Motion Estimation and Compensation Method for Ultrahigh-Resolution Airborne Stepped-Frequency SAR With Long Integration Time

For the ultrahigh-resolution airborne stepped-frequency synthetic aperture radar, very large synthetic bandwidth and very long integration time may lead to a 2-D space-variant (SV) motion error when the aircraft flies off the ideally straight trajectory due to the atmospheric turbulence. This new type of error complicates the motion estimation and motion compensation (MOCO). For the motion estimation, we present a jointly 2-D SV motion error estimation method to simultaneously consider the range-variant motion error and the azimuth-variant motion error. For the MOCO, we propose a 2-D SV-MOCO method. The method is implemented through three processing steps: 1) two-step MOCO for the space-invariant motion error and the range-variant phase error; 2) range block-based chirp-z transform (CZT) for the range-variant envelope error; and 3) range block division for the range-dependent azimuth-variant phase error based on the azimuth subaperture method. Finally, processing of simulated data and real data validates the proposed methods.

Elastomer composition properties forecast using percolation model

The results of theoretical and experimental studies on development of methodology for assessing geometric parameters elastomeric phase morphology compositions based on an analysis of the problems in percolation and Ferhyulsta differential equation. Established that the geometric probability phase transitions, a given content heterophase sharply reduced in case heterophase particle size reduction. With the developed technique Calculations geometric parameters of the rubber phase morphology to assess the impact of the nature and content all ingredients on the structure and properties of rubber. The principles of construction the optimal composition of elastomer (rubber and polyurethane block). Established that maximum deformation-strength properties sold in case formation of morphological structure with a minimum particle size heterophase. The optimal block for polyurethanes morphology is that, on the one hand, provides the highest probability of preservation of properties for heterophase rigid blocks providing high strength, on the other hand - saving properties of variance environment for high elasticity. The results, based on analysis in problems of percolation well aligned with known experimental data, by light scattering, by optical and electron microscopy. The approach has practical value for optimization of elastomeric compositions, as well as to developing brand range block polyurethanes.

Fractal Image Compression Based on Complex Exponent Moments and Fuzzy Clustering

The encoding in fractal image compressions are very time-consuming, because a large numbers of sequential search through a list of domains are needed to find the best match for a given range block. The Complex Exponent Moments (CEMs) are shift, rotation, scale and intensity distorted-invariant. This invariance can be used to match fractal image, and 2-D Fast Fourier Transform (FFT) algorithm is easily used to calculate CEMs. An effective fractal image compression based on CEMs and fuzzy clustering is proposed in this paper. Firstly, domain blocks are categorized using fuzzy c-mean-clustering approach. Then range blocks are compared to find the best domain blocks based on the CEMs. It shows in experimental results that the encoding is speed up with better performance in contrast with other fractal algorithms.

Comparative analysis of edge-based fractal image compression using nearest neighbor technique in various frequency domains

In this paper, edge based fractal image compression is proposed for various frequency domains. Range and domain blocks of similar edge property are mapped to lowest DCT coefficient in a vertical and horizontal direction into 2D coordinate System. The proposed algorithm is hybrid approaches which exercise fast Fourier transform (FFT), discrete cosine transform (DCT) and real DCT on edge based FIC. Range and domain block pool searching is proposed using k-nearest neighbor search method. Here dissimilarity or distance measure is used in K-NN search for domain and range block. Three frequency transform is used to compare the performance of edge based FIC for K-NN search strategies. The proposed algorithm focuses on image encoding time consumed in edge based FIC with minimal distortion in the resultant image. Proposed algorithm shows improvement in the quality of decoded image with same encoding time and compression ratio as compared to the existing algorithm. The quality of the decoded image was measured regarding PSNR.

Adaptive searchless fractal image compression in DCT domain

In this paper, an adaptive searchless fractal image compression technique in the frequency domain is proposed. Proposed methods aim to improve image compression fidelity criteria in discrete cosine transform domain (DCT). In this work spatial domain, adaptive methods are exercised in discrete cosine domain for optimisation of image encoding. Variance-based selection of image blocks and adaptive threshold are two simple adaptive techniques administered on attaining objectives of image compression. First, range block exclusion adaptive method removes homogenous range blocks before FIC coding. This exclusion consequently recedes the number of range-domain blocks matching operations. Proposed hybrid compression algorithm localises domain block location about range block without searching entire domain pool. Second technique implemented is domain pool size reduction. Variances of range and domain blocks are compared. Domain blocks that are within threshold value are kept in the pool. Finally to further optimise the performance of encoding adaptive threshold in quadtree partitioning is applied in along with variance domain selection approach. Simulation results substantiate that adaptive techniques have higher coding performance in terms of encoding time, peak signal to noise ratio and compression ratio in contrast to the fractal image encoding without adaptive techniques.