<title>Regional adaptive resolution-based fractal block coding</title>

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.

Many effective methods have been proposed to solve the problem of image compression. Fractal image code can compress an image in a higher compress ratio level, the fundamental idea of fractal image compression is based on the iteration function system (IFS). In this paper, we proposed a novel method based on IFS with probabilities can be used in image compression and image retrieval, we obtain enlightenment from fractal no search methods, our method also no need to carried out searching stage when compress the image, so the consuming time with image compression is less than traditional methods. We utilized the image code as index file, and apply our method in image retrieval, experiment results indicate that our method is effective in image retrieval.

Fractal image coding is a novel technique for still image compression. Compared with the distance between the range block and the matching domain block, setting of the initial threshold value is one of the most difficult problems in Fisher Quadtree-based fractal image coding. In this paper, a novel fractal image coding based on Quadtree partition of the adaptive threshold value is proposed. Considering the input image feature fully, we put forward the computation derivation process of the adaptive threshold value progressively and declare that the adaptive threshold value has the direct proportion with the variance of the current range block. Experimental results show that compared with Fisher Quadtree-based fractal coding for the same image, the proposed coding scheme obtains better performance including the improved quality of the decoded image, shorter compression time and higher compression ratio.

In this study, the performance of fractal‐based coding algorithms such as standard fractal coding, quasi‐lossless fractal coding and improved quasi‐lossless fractal coding are evaluated by investigating their ability to compress magnetic resonance images (MRIs) based on compression ratio, peak signal‐to‐noise ratio and encoding time. For this purpose, MRI head scan test sets of 512 × 512 pixels have been used. A novel quasi‐lossless fractal coding scheme, which preserves important feature‐rich portions of the medical image, such as domain blocks and generates the remaining part of the image from it, has been proposed using fractal transformations. One of the biggest tasks in fractal image compression is reduction of encoding computation time. A machine learning‐based model is used for reducing the encoding time and also for improving the performance of the quasi‐lossless fractal coding scheme. The results show a better performance of improved quasi‐lossless fractal compression method. The quasi‐lossless and improved quasi‐lossless fractal coding algorithms are found to outperform standard fractal coding thereby proving the possibility of using fractal‐based image compression algorithms for medical image compression. The proposed algorithm allows significant reduction of encoding time and also improvement in the compression ratio.

Approximation of an image by the attractor evolved through iterations of a set of contractive maps is usually known as fractal image compression. The set of maps is called iterated function system (IFS). Several algorithms, with different motivations, have been suggested towards the solution of this problem. But, so far, the theory of IFS with probabilities, in the context of image compression, has not been explored much. In the present article we have proposed a new technique of fractal image compression using the theory of IFS and probabilities. In our proposed algorithm, we have used a multiscaling division of the given image up to a predetermined level or up to that level at which no further division is required. At each level, the maps and the corresponding probabilities are computed using the gray value information contained in that image level and in the image level higher to that level. A fine tuning of the algorithm is still to be done. But, the most interesting part of the proposed technique is its extreme fastness in image encoding. It can be looked upon as one of the solutions to the problem of huge computational cost for obtaining fractal code of images.

In this paper, we present an efficient hybrid image compression method. According to Human eye sensitivity, It is not sensitive to gray-scale error, but the human eye is particularly sensitive to the image edge features, through we research strengths and weaknesses of the fractal image coding and SPIHT(set partitioning in hierarchical trees) algorithm, proposed a hybrid algorithm based on SPIHT and Fractal image compression algorithm, it makes full use of landscape features and human visual characteristics, the image is decompounded into low frequency sub-band and high frequency sub-band, then the low frequency sub-band is uses fractal coding, this could make the low-frequency information loss less who is sensitivity to human visual signal and the high frequency sub-band is uses SPIHT coding, so encoding time greatly reduced. Experimental results show that the Hybrid Image Compression Algorithm not only raises the coding efficiency and reconstructed image quality but also reduces the image encoding time.

DOI: 10.1049/ic.2009.0127 ISBN: 978-1-84919-193-7 Location: Maharashtra, India Conference date: 19-20 Sept. 2009 Format: PDF Many conventional methods of face recognition depend solely on appearance and model based. But there is inbuilt degree of self similarity in the image of faces which can be efficiently utilized through representation exploiting self-transformations, known as iterated function system (IFS). Interestingly, virtually all images of natural or man-made objects, show region wise self similarity although they may not be globally self similar. Such objects can be represented by partitioned iterated function system (PIFS) very compactly. Hence, the goal is to carry out the face recognition using partitioned iterated function system. This approach has been tested upon the 106 images of 27 persons. It is giving the good results under the variance, rotation and scaling. In this approach we carried out face recognition based on PIFS representation and matching carried out in the PIFS code domain, which is more efficient than correlation in the image domain. The recognition method is attractive from the view point of execution time as the PIFS code of reference faces are built off-line and recognition of query object involves only comparison of its PIFS code with those in the database. (5 pages) Inspec keywords: face recognition; image matching; iterative methods Subjects: Interpolation and function approximation (numerical analysis); Image recognition; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques

Fractal image compression is a lossy image coding method using partitioned iterated function systems (PIFS). Compared with rapid decompression algorithms, the compression process is extremely time-consuming, so how to speed up the compression procedure remains a challenging issue. The most common solution involves classification of domain and range blocks according to features, after which matches across class boundaries are excluded. We compare two feature vector methods - mass center and box dimension. Experimental results demonstrate the improvements in compression performance.

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.

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.

This paper proposes a new quality improvement technique for fractal-based image compression techniques using diffusion equations. Fractal coding uses a contractive mapping scheme to represent an image. This process of contractive mapping causes artifacts and blocking effects in encoded images. This problem is severed when compression ratio is increased or there are high frequency regions in the image. Hence, to amulet the deficiency of fractal coding approaches in image compression, we propose using diffusion equations as a post processor. Diffusion equations are powerful tools for image enhancement. This technique has been examined on a variety of standard images. The obtained results indicate that the proposed method improves performance of fractal-based image compression techniques.

Constructions of the (global) fractal interpolation functions on standard function spaces got a lot of attention in the last centuries. Motivated by the newly introduced local fractal functions corresponding to a local iterated functions system which is the generalization of the traditional iterated functions system we construct the local non-affine alpha - fractal functions in this article. A few examples of the graphs of these functions are provided. A fractal operator which takes the classical function to its local fractal counterpart is defined and some of its properties are also studied.

One of the main disadvantages of fractal image data compression is a loss time in the process of image compression (encoding) and conversion into a system of iterated functions (IFS). In this paper, the idea of the inverse problem of fixed point is introduced. This inverse problem is based on collage theorem which is the cornerstone of the mathematical idea of fractal image compression. Then this idea is applied by iterated function system, iterative system functions and grayscale iterated function system down to general transformation. Mathematical formulation form is also provided on the digital image space, which deals with the computer. Next, this process has been revised to reduce the time required for image compression by excluding some parts of the image that have a specific milestone. The neural network algorithms have been applied on the process of compression (encryption). The experimental results are presented and the performance of the proposed algorithm is discussed. Finally, the comparison between filtered ranges method and self-organizing method is introduced.

Roundoff error due to iterative computation with finite wordlength degrades the quality of decoded images in fractal image coding that employs a deterministic iterated function system. This paper presents a state-space approach to roundoff error analysis of fractal image coding for grey-scale images. The output noise variance matrix and the noise matrix are derived for the measures of error and the output noise variance is newly defined as the pixel mean of diagonal elements of the output noise matrix. A quantitative comparison of experimental roundoff error with analytical result is made for the output noise variance. The result shows that our analysis method is valid for the fractal image coding. Our analysis method is useful to design a real-time and low-cost decoding hardware with finite wordlength for fractal image coding.

Still (or intra-picture) image coding includes compression of binary images and images with multiple amplitude levels, the latter case either monochrome or with multiple color components, as typically captured by cameras or image scanners. Beyond that, compression schemes are also applied to other sensor data such as magnetic resonance intensity in medical imaging, infrared, x-ray, depth captured by range sensors, or to synthetic content, specifically generated by computer graphics or captured from screens. Various methods such as run-length coding and conditional entropy coding for binary images; vector quantization, predictive coding and transform coding for multi-amplitude images are presented in more detail. Further sections discuss lossless image compression, synthesis based coding, fractal coding and 3D image coding. As examples for the overall combination of building blocks, still image compression standards like JPEG, JPEG 2000 and the intra-picture compression of AVC and HEVC video coding standards are discussed in detail.KeywordsSide InformationHigh Efficiency Video CodeJoint Photographic Expert GroupPredictive CodeIntra PredictionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.