A feature­based approach to fractal image compression

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.

The image partitioning scheme in fractal image compression is one of the important aspect for enhancement of performance. In this paper, an adaptive quardtree partitioning scheme is proposed where the entire image is sub-divided recursively into four sub-images. The partitioning points are selected adaptively in a image context-dependent way instead of middle points of the image sides as in quardtree partitioning scheme. Biased successive differences of sum of pixel values of rows of the image are calculated to divide the image row-wise into two sub-images. Then, each sub-image is farther divided column-wise into two parts using biased successive differences of sum of pixel values of columns of the subimage. Then, a fractal image compression technique is proposed based on the proposed partitioning scheme. The comparison of the compression ratio and PSNR are done between fractal image compression with quardtree and proposed adaptive quardtree partitioning schemes. The comparison of the compression time between the same is also done. The fractal image compression with proposed partitioning scheme offers better compression rates most of the times with comparatively improved PSNR. But, the compression time of the fractal image compression with proposed partitioning scheme is much more than its counterpart.

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.

Images are compressed using lossy and lossless compression schemes to utilize the Bandwidth in an effective way and also to provide enough space for storing voluminous data. Fractal Image Compression (FIC), is one such lossy compression scheme based on contractive mapping theorem. Affine transforms are being employed in Fractal Image compression to map the range blocks and domain blocks incorporating the property of self similarity in the images. As there is a need to enhance the performance measures like compression ratio (CR) and peak signal to noise ratio (PSNR), an attempt is made in this research paper to analyze FIC using Quad-tree Partitioning technique and Embedded Block Coding Optimization Truncation (EBCOT) technique for Medical Images. Here in this paper FIC with EBCOT is being implemented on Medical Images like CT of Bone and MR Images of Brain and the Performance measures like CR, PSNR at different Threshold Values were measured. Mat lab simulated results for these Performance Measures showed that the PSNR and CR is high when FIC is performed along with EBCOT Encoding.

In this paper an adaptive algorithm is proposed to reduce the long time that has been taken in the Fractal Image Compression (FIC) technique. This algorithm worked on reducing the number of matching operations between range and domain blocks by reducing both of the range and domain blocks needed in the matching process, for this purpose, two techniques have been proposed; the first one is called Range Exclusion ( RE ), in this technique variance factor is used to reduce the number of range blocks by excluding ranges of the homogenous or flat regions from the matching process; the second technique is called Reducing the Domain Image Size ( RDIZ ), it is responsible for the reduction of the domain by minimizing the Domain Image Size to 1/16 th instead of 1/4 th of the original image size used in the traditional FIC. This in turn will affect the encoding time, compression ratio and the reconstructed image quality. For getting best results, the two techniques are coupled in one algorithm; the new algorithm is called ( RD-RE ). The tested (256x256) gray images are partitioned into fixed (4x4) blocks and then compressed using visual C++ 6.0 code. The results show that RE technique is faster and gets more compression ratio than the traditional FIC and keeping a high reconstructed images quality while RD-RE is faster and it gets higher compression ratio than RE but with slight loss in the reconstructed image quality.

This work presents Fixed Point Theorems (FPT) on Digital Images (DI) based on the Banach contraction concept developed. The study aims to develop the application of Banach Contraction Mapping Theory (BCMT) for DI, which was presented. The following finding generates a single Fixed Point (FP) for DI, exploring the core idea of DI and implementing the FPT in the context of Digital Image Compression (DIC). Fractal Image Compression (FIC) is a standard method for DIC. It is founded on a search for an object that is accurately in the image. However, a significant problem with DIC is its computational weight. The study proposed a method to reduce data transmission time by applying Image Compression (IC). It highlights the challenges of maximizing Image Quality (IQ) or reducing transmission time for definite IQ. The classic FIC method, which uses non-linear contractive mapping as a constant contractive factor, can attain this.

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.

The main problem with all fractal compression implementation is execution time. Algorithms can spend hours to compress a single image. Most of the major variants of the standard algorithm for speeding up computation time have led to a bad-quality or a lower compression ratio. For example, the Fisher's [7] proposed classification pattern greatly accelerated the algorithm, but image quality was poor due to the search-space reduction imposed by the classification, which eleminates a lot of good solutions. By using genetic algorithms to address the problem, we optimize the domain blocks search. We explore all domain blocks present in the image but not in exhaustive way (like a standard algorithm) and without omitting any possible block (solution) as a classification pattern does. A genetic algorithm is the unique method for satisfying these constraints. And it is a way to do be a random search because the genetic one is directed by fitness selection, which produces optimal solutions. Our goal in this work is to use a genetic algorithm to solve the IFS inverse problem and to build a fractal compression algorithm based on the genetic optimization of a domain blocks search. we have also implemented standard Barnsley algorithm, the Y. Fisher based on classification, and the genetic compression algorithm with quadtree partitioning. A population of transformations was evolved for each range block, and the result is compared with the standard Barnsely algorithm and the Fisher algorithm = based classification. We deduced an optimal set of values for the best parameters combination, and we can also specify the best combination for each desired criteria: best compression ratio, best image quality, or quick compression process. By running many test images, we experimentally found the following set of optimal values of all the algorithm parameters that ensure compromise between execution time and solutions optimality: Population size = 100, Maximum generations = 20, Crossover rate = 0.7, Mutation rate = 0.1, RMS limit = 5, Decomposition error limit = 10, Flips and isometrics count = 8. In our proposed algorithm, results were much better than those obtained both vences and Rudomin [5] and Lankhorst [4] approaches.

The demand for images in video sequences and computer animation has increased drastically over the years. This gives attention on an important issue of compression, resulting into reduction in cost of data storage and transmission. For still image compression JPEG is used world -wide. But alternative methods are also being explored; Fractal image compression is one of them. It is based on the self-similarity property to find out the best match within the image itself. This property is used to generate a fractal code. In this paper, the new approach for fractal image compression using genetic algorithm with ranking select mechanism is proposed. This proposed algorithm is applied on fractal as well as non-fractal images and the experimental result shows that the encoding time for both types of images is greatly reduced while maintaining their quality intact.

This paper presents a quadtree partitioning fractal image compression (QPFIC) method used for the partial discharge (PD) image remote recognition system. Self-similarity in PD images is the premise of fractal image compression and is described for the typical PD images acquired from defect model experiments in laboratory. Influences of fractal image compression on a group of PD image features are discussed. Fifty PD data samples are used to qualify the QPFIC to be used in remote PD pattern recognition. Analysis results show that the QPFIC method produces errors of the computational features. Such errors could not influence the PD image recognition results under the control of the PD image compression errors.

Fractals are geometric patterns generated by Iterated Function System theory. A popular technique known as fractal image compression is based on this theory, which assumes that redundancy in an image can be exploited by block-wise self-similarity and that the original image can be approximated by a finite iteration of fractal codes. This technique offers high compression ratio among other image compression techniques. However, it presents several drawbacks, such as the inverse proportionality between image quality and computational cost. Numerous approaches have been proposed to find a compromise between quality and cost. As an efficient optimization approach, genetic algorithm is used for this purpose. In this paper, a crowding method, an improved genetic algorithm, is used to optimize the search space in the target image by good approximation to the global optimum in a single run. The experimental results for the proposed method show good efficiency by decreasing the encoding time while retaining a high quality image compared with the classical method of fractal image compression.

This research explores the principles, implementation, and optimization of a competitive volume compression system based on fractal image compression. The extension of fractal image compression to volumetric data is trivial in theory. However, the simple addition of a dimension to existing fractal image compression algorithms results in infeasible compression times and noncompetitive volume compression results. This paper extends several fractal image compression enhancements to perform properly and efficiently on volumetric data, and introduces a new 3D edge classification scheme based on principal component analysis. Numerous experiments over the many parameters of fractal volume compression suggest aggressive settings of its system parameters. At this peak efficiency, fractal volume compression surpasses vector quantization and approaches within 1 dB PSNR of the discrete cosine transform. When compared to the DCT, fractal volume compression represents surfaces in volumes exceptionally well at high compression rates, and the artifacts of its compression error appear as noise instead of deceptive smoothing or distracting ringing.

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.

Images occupy the increasing part of the medical information systems. The Large volume of image contents needs to be compressed for effective storage and transmission. Fractal Image Compression (FIC) is a technique based on Quad tree decomposition. This compression represents an image by a contractive Affine transforms. In the existing literatures this technique has been analyzed mostly on natural images and found to be efficient. In this paper an attempt is made with medical images namely MRI, CT, Angiogram and Ultrasounds. The performance of FIC is compared with traditional SPIHT compression technique in terms of PSNR, compression Ratio, Encoding and Decoding time. From the simulation results, it is inferred that FIC technique outperforms SPIHT in terms of compression ratio with acceptable quality of reconstructed image which makes it suitable for medical image transmission applications.

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