Fractal Image Compression Using Genetic Algorithm
20 Jun 2010-pp 241-243
TL;DR: This paper gives the improved method of generating a binary image IFS using Genetic Algorithm, which measures the similarity between the attractor and the image that penalizes a large number of maps and high contractivity factors.
Abstract: This paper gives the improved method of generating a binary image IFS using Genetic Algorithm. To find the maps of IFSs that can encode black and white (BW) images, the Genetic Algorithm uses a variable-length genotype representation, i.e., each IFS is represented as a list of maps, and a map is represented as a set of real parameters. Special genetic operators that maintain and control the feasibility of the individuals in the population are adopted. A fitness function is defined that measures the similarity between the attractor and the image that penalizes a large number of maps and high contractivity factors.
Citations
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TL;DR: Efficiency of a fractal-based image compression technique that applies adaptive quadtree partitioning and archetype classification schemes is discussed and modified versions yield not only better image quality but also high encoding speed than their counterparts.
Abstract: The DCT-based JPEG image compression technique is a standard for lossy image compression. But the technique is not suitable at high compression ratios. Again, it is resolution dependent. Alternativ...
7 citations
01 Sep 2015
TL;DR: A technique for lossless compression of significant parts by extracting the region of interest in DICOM images using Huffman encoding and genetic algorithm for the enhancement of compression ratio is proposed.
Abstract: The numerous medical images generated by various imaging devices require a huge amount of storage space and minimum transmission bandwidth. So, it is necessary to use an appropriate technique for compression of medical images in order to meet the above concerns. This paper proposes a technique for lossless compression of significant parts by extracting the region of interest in DICOM images. The extracted region is compressed using Huffman encoding and genetic algorithm for the enhancement of compression ratio. Additionally, the value of PSNR has also been calculated. When the results of the proposed technique are compared with the previous method, it is found that there is an increase of more than 400% in the compression ratio. It also provides an average increase of 20% in the compression ratio by using Huffman encoding.
6 citations
TL;DR: A fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality.
Abstract: Fractal image compression techniques are now very popular for its high compression rates and resolution independence property. However, the qualities of decoded images of the existing techniques are not satisfactory. An adaptive partitioning scheme can improve the image quality significantly. These existing adaptive techniques use linear affine maps during encoding that have limited pixel intensity approximation ability. In order to increase the image quality further, non-linear affine maps can be used that generalizes the pixel intensity approximation and generates much better approximation. Here, a fractal based technique for image compression using non-linear contractive affine maps has been proposed that applies adaptive quadtree partitioning to partition image in a context dependent way to enhance decoded image quality. The technique partitions twice an image to be compressed to obtain collection of ranges and domains and finds the highest matching non-linear affine transformed domain of each range. The corresponding affine parameters are kept in the compressed file. However, a range may be broken into sub-ranges using adaptive quadtree partitioning for unavailability of enough matching domains and repeat the same on those. The comparative results show that the proposed technique greatly improves the decoded image quality than existing techniques and also maintains the high compression ratios. Two variants have also been proposed that improve compression ratio of the proposed technique without any degradation of image quality using loss-less coding.
5 citations
Cites methods from "Fractal Image Compression Using Gen..."
...[6] used a variable genetype representation of IFS to improve fractal coding....
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TL;DR: The hybrid method which combines Fractal quad tree with wavelet and Huffman coding is implemented and compared different parameters as compression ratio and the compression time of the proposed method with the existing methods.
Abstract: Fractal image compression offers high compression ratios and quality image reconstruction. It uses various techniques as the fractal with DCT, wavelet, neural network, genetic algorithms, quantum acceleration etc. Additionally, because fractals are infinitely magnifiable, fractal compression is resolution independent and so a single compressed image can be used efficiently for display in any image resolution including resolution higher than the resolution of the original image. Breaking an image into pieces and identifying selfsimilar ones is the main principle of the approach. In this paper, the different issues in fractal image compression as partitioning, larger encoding time, compression ratio, quality of the reconstructed image, decoding time, SSIM(Structured Similarity Index) are discussed and highlighted. The various areas for improvement as larger encoding time and PSNR are also suggested. The various parameters for evaluating the performance of these techniques as PSNR, compression ratio, encoding time, and decoding time are also suggested. Comparison of Fractal techniques for color image, texture and satellite image is done using different parameters as compression time, compression ratio and PSNR. The hybrid method which combines Fractal quad tree with wavelet and Huffman coding is implemented and compared different parameters as compression ratio and the compression time of the proposed method with the existing methods.
3 citations
Cites methods from "Fractal Image Compression Using Gen..."
...Some genetic algorithms [44-45] are also used to improve searching for the domain block....
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01 Jan 2020
TL;DR: Using the property of pattern adaption of surroundings, cuttlefish optimisation algorithm is applied to minimise the time taken for fractal coding, and results have been compared with other meta-heuristic techniques, and has shown high compression ratio.
Abstract: Increase in demand for better appearance and less storage requirement of an image has led to explore various image compression techniques. Due to technological advancement of photo capturing devices such as single-lens reflex camera (SLR), digital SLR, smart phone cameras, and satellite sensors, more detailed information can be recorded in a single image. A coloured image captured by high wavelength sensors produces large-sized image as it contains highly correlated data. Many image compression and analysis techniques have been developed to aid the interpretation of images and to compress as much information as possible in it. The goal of image compression is to recreate original image with less number of bits and minimal data loss. For generating computer graphic images and compression of objects, it has been suggested that by storing images in the form of transformation instead of pixels lead to compression and can be achieved through fractal coding. In fractal image compression, encoding image blocks into fractal codes using iterated function system (IFS) takes large amount of time taken to compress it. A study of various meta-heuristics techniques, which are designed to solve complex problems approximately, has been conducted to improve upon computational time of fractal coding as well as compression ratio, while maintaining image visually. In this paper, using the property of pattern adaption of surroundings, cuttlefish optimisation algorithm is applied to minimise the time taken for fractal coding. Compression results have been compared with other meta-heuristic techniques, such as particle swarm optimisation and genetic algorithm, and has shown high compression ratio of approximately 31%.
References
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Book•
12 Oct 2011TL;DR: Working C code for a fractal encoding/decoding scheme capable of encoding images in a few seconds, decoding at arbitrary resolution, and achieving high compression rations is proposed.
Abstract: From the contents: Recent theoretical results on fast encoding and decoding methods, various schemes for encoding images using fractal methods, and theoretical models for the encoding/decoding process.- Working C code for a fractal encoding/decoding scheme capable of encoding images in a few seconds, decoding at arbitrary resolution, and achieving high compression rations.- Experimental results from various schemes showing their capability and forming the basis for a sophisticated implementation.- A list of previously unresearched projects containing both new ideas and inhancements to the schemes discussed in the book.- A comparison of the fractal schemes in the book with JPEG, commercial fractal software, and wavelet methods.
1,098 citations
Book•
01 Jan 1991
TL;DR: In this article, the authors present a guide to data compression techniques, including Shannon-Fano and Huffman coding techniques, lossy compression, JPEG compression algorithm, and fractal compression.
Abstract: From the Publisher:
Topics in this guide to data compression techniques include the Shannon-Fano and Huffman coding techniques, Lossy compression, the JPEG compression algorithm, and fractal compression. Readers also study adaptive Huffman coding, arithmetic coding, dictionary compression methods, and learn to write C programs for nearly any environment. The disk illustrates each learned technique and demonstrates how data compression works.
618 citations
Book•
01 Jul 1991
TL;DR: In this paper, the authors present a guide to data compression techniques, including Shannon-Fano and Huffman coding techniques, lossy compression, JPEG compression algorithm, and fractal compression.
Abstract: From the Publisher:
Topics in this guide to data compression techniques include the Shannon-Fano and Huffman coding techniques, Lossy compression, the JPEG compression algorithm, and fractal compression. Readers also study adaptive Huffman coding, arithmetic coding, dictionary compression methods, and learn to write C programs for nearly any environment. The disk illustrates each learned technique and demonstrates how data compression works.
548 citations
TL;DR: Both theoretical analysis and experimental results show a higher compression ratio with better quality images by using the proposed algorithm.
Abstract: This paper presents an improved method of generating a binary image affine IFS (iterated function system) by using genetic algorithm We adopt a natural variable-length genotype encoding to represent an individual The multiobject fitness function is also applied in this algorithm In addition, a distributed version of the binary image compression algorithm is implemented Both theoretical analysis and experimental results show a higher compression ratio with better quality images by using the proposed algorithm
20 citations
"Fractal Image Compression Using Gen..." refers background or methods in this paper
...[1]....
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...…intrinsic parallelism and some intelligent properties such as adaptation, self-organizing, and self-learning, the Genetic Algorithm (GA) and more generally the evolutionary algorithm (EA) are currently efficient stochastic optimization tools, and are widely used in various application fields [1]....
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...Furthermore, GA is also an efficient searching method for approximations to global optimal in the huge and complicated space in relatively short time....
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TL;DR: An evolutionary algorithm is used to search for iterated function systems (IFS) that can encode black and white images with variable-length genotype, and special genetic operators that maintain and control their feasibility are defined.
Abstract: An evolutionary algorithm is used to search for iterated function systems (IFS) that can encode black and white images. As the number of maps of the IFS that encodes an image cannot be known in advance, a variable-length genotype is used to represent candidate solutions, Accordingly, feasibility conditions of the maps are introduced, and special genetic operators that maintain and control their feasibility are defined, In addition, several similarity measures are used to define different fitness functions for experimentation. The performance of the proposed methods is tested on a set of binary images, and experimental results are reported.
15 citations
"Fractal Image Compression Using Gen..." refers background in this paper
...The objective of fractal image compression is to ultimately find the best IFS, and hence Genetic Algorithms suit the current application [5]....
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...{ }niRRwi ,,2,1: 22 …=→ An affine transformation is a mathematical function made up from some combination of a rotation, a scale, a skew, a stretch and a translation in n-dimensional space....
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