CRITICAL REVIEW OF FRACTAL IMAGE COMPRESSION

1995 ◽  
Vol 06 (01) ◽  
pp. 47-66 ◽  
Author(s):  
HARRI RAITTINEN ◽  
KIMMO KASKI
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Compression Ratio ◽  
Critical Review ◽  
Quality Measures ◽  
Fractal Image Compression ◽  
Fractal Compression ◽  
New Methods ◽  
Fractal Image ◽  
Image Quality Measures

In this paper, fractal compression methods are reviewed. Three new methods are developed and their results are compared with the results obtained using four previously published fractal compression methods. Furthermore, we have compared the results of these methods with the standard JPEG method. For comparison, we have used an extensive set of image quality measures. According to these tests, fractal methods do not yield significantly better compression results when compared with conventional methods. This is especially the case when high coding accuracy (small compression ratio) is desired.

scholarly journals Improving Fractal Image Compression Scheme through Quantization Operation

2007 ◽  
Vol 4 (2) ◽  
pp. 330-337
Author(s):  
Baghdad Science Journal
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
New Method ◽  
Compression Scheme ◽  
Fractal Image Compression ◽  
High Image Quality ◽  
Fractal Image ◽  
Separate Step ◽  
Transform Coefficients

We explore the transform coefficients of fractal and exploit new method to improve the compression capabilities of these schemes. In most of the standard encoder/ decoder systems the quantization/ de-quantization managed as a separate step, here we introduce new way (method) to work (managed) simultaneously. Additional compression is achieved by this method with high image quality as you will see later.

scholarly journals Nouvelle approche d’accélération du codage fractal d’images

2009 ◽  
Vol Volume 11, 2009 - Special... ◽  
Author(s):  
Sofia Douda ◽  
Abdelhakim El Imrani ◽  
Mohammed Limouri
Keyword(s):  
Computational Complexity ◽  
Image Quality ◽  
Compression Ratio ◽  
Classification Methods ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Fast Decoding ◽  
Reduction Methods ◽  
Speed Up ◽  
International Audience

International audience The Fractal image compression has the advantage of presenting fast decoding and independent resolution but it suffers of slow encoding phase. In the present study, we propose to reduce the computational complexity by using two domain pools instead of one domain pool and encoding an image in two steps (AP2D approach). AP2D could be applied to classification methods or domain pool reduction methods leading to more reduction in encoding phase. Indeed, experimental results showed that AP2D speed up the encoding time. The time reduction obtained reached a percentage of more than 65% when AP2D was applied to Fisher classification and more than 72% when AP2D was applied to exhaustive search. The image quality was not altered by this approach while the compression ratio was slightly enhanced. La compression fractale d’images permet un décodage rapide et une indépendance de la résolution mais souffre d’une lenteur dans le codage. Le présent travail présente une approche visant à réduire le temps de calcul en utilisant deux dictionnaires et une approximation de l’image en deux étapes (AP2D). L’approche AP2D peut être appliquée aux méthodes de classification ou aux méthodes de réduction du cardinal du dictionnaire et ainsi réduire davantage le temps de codage. Les résultats expérimentaux ont montré que AP2D appliquée à une recherche exhaustive a atteint un gain de temps de plus de 72%. De même AP2D appliquée à la classification de Fisher a permis une réduction de temps de codage de plus de 65%. La qualité de l’image n’a pas été altérée par cette approche et le taux de compression a légèrement augmenté.

scholarly journals A NOVEL FAST FRACTAL IMAGE COMPRESSION METHOD BASED ON DISTANCE CLUSTERING IN HIGH DIMENSIONAL SPHERE SURFACE

Fractals ◽  
2017 ◽  
Vol 25 (04) ◽  
pp. 1740004 ◽  
Author(s):  
SHUAI LIU ◽  
ZHENG PAN ◽  
XIAOCHUN CHENG
Keyword(s):  
Image Compression ◽  
High Dimensional ◽  
Dimensional Sphere ◽  
Compression Method ◽  
Sphere Surface ◽  
Fractal Image Compression ◽  
Fractal Compression ◽  
Fractal Image ◽  
Speed Up ◽  
Encoding Method

Fractal encoding method becomes an effective image compression method because of its high compression ratio and short decompressing time. But one problem of known fractal compression method is its high computational complexity and consequent long compressing time. To address this issue, in this paper, distance clustering in high dimensional sphere surface is applied to speed up the fractal compression method. Firstly, as a preprocessing strategy, an image is divided into blocks, which are mapped on high dimensional sphere surface. Secondly, a novel image matching method is presented based on distance clustering on high dimensional sphere surface. Then, the correctness and effectiveness properties of the mentioned method are analyzed. Finally, experimental results validate the positive performance gain of the method.

scholarly journals BASED ON RANGE AND DOMAIN FRACTAL IMAGE COMPRESSION OF SATELLITE IMAGERIES IMPROVED ALGORITHM FOR RESEARCH

2014 ◽  
pp. 71-74
Author(s):  
SAEMA ENJELA ◽  
A.G. ANANTH
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Fractal Coding ◽  
Fractal Compression ◽  
Urban Image ◽  
Fractal Image ◽  
Domain Block ◽  
Improved Algorithm

Fractal coding is a novel method to compress images, which was proposed by Barnsley, and implemented by Jacquin. It offers many advantages. Fractal image coding has the advantage of higher compression ratio, but is a lossy compression scheme. The encoding procedure consists of dividing the image into range blocks and domain blocks and then it takes a range block and matches it with the domain block. The image is encoded by partitioning the domain block and using affine transformation to achieve fractal compression. The image is reconstructed using iterative functions and inverse transforms. However, the encoding time of traditional fractal compression technique is too long to achieve real-time image compression, so it cannot be widely used. Based on the theory of fractal image compression; this paper raised an improved algorithm form the aspect of image segmentation. In the present work the fractal coding techniques are applied for the compression of satellite imageries. The Peak Signal to Noise Ratio (PSNR) values are determined for images namely Satellite Rural image and Satellite Urban image. The Matlab simulation results for the reconstructed image shows that PSNR values achievable for Satellite Rural image ~33 and for Satellite urban image ~42.

scholarly journals Fractal Image Compression Based on High Entropy Values Technique

2018 ◽  
Vol 28 (2) ◽  
pp. 119
Author(s):  
Douaa Younis Abbaas
Keyword(s):  
Image Quality ◽  
Compression Ratio ◽  
Search Algorithm ◽  
Fractal Image Compression ◽  
High Entropy ◽  
Fractal Image ◽  
Reconstruction Image ◽  
Domain Block ◽  
Entropy Technique ◽  
Full Search

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.

Some new methods to fractal image compression

1997 ◽  
Vol 2 (2) ◽  
pp. 80-85
Author(s):  
Yipeng Shi ◽  
Wei Gu ◽  
Liming Zhang ◽  
Shoujie Chen
Keyword(s):  
Image Compression ◽  
Fractal Image Compression ◽  
New Methods ◽  
Fractal Image

scholarly journals Fractal Image Compression Using Canonical Huffman Coding

2020 ◽  
Vol 15 (1) ◽  
pp. 91-105
Author(s):  
Shree Ram Khaitu ◽  
Sanjeeb Prasad Panday
Keyword(s):  
Image Compression ◽  
Lossy Compression ◽  
Digital Data ◽  
Huffman Coding ◽  
Compression Scheme ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Fractal Compression ◽  
Fractal Image ◽  
Entropy Encoding

 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.  

Face Detection Based on the Theory of Fractal Compression

2014 ◽  
Vol 945-949 ◽  
pp. 1825-1829
Author(s):  
Qing Sen An ◽  
Yue Bin Chen ◽  
Jing Fan ◽  
Jin Long Wang
Keyword(s):  
Image Compression ◽  
Face Detection ◽  
Iterated Function Systems ◽  
Fractal Image Compression ◽  
Fractal Compression ◽  
Fractal Image ◽  
Iterated Function ◽  
The Face ◽  
Face Similarity

The face detection has been a very important issue, the use of local and global face similarity between faces can be detected. In this paper, based on fractal image compression theory, we construct a local iterated function systems as a description of the face to detect the face.

Fast Fractal Image Compression by Kicking Out Similar Domain Images

2018 ◽  
pp. 318-331
Author(s):  
Shilpi Sharma ◽  
Arvind Kumar Kourav ◽  
Vimal Tiwari
Keyword(s):  
Image Quality ◽  
Image Compression ◽  
Standard Test ◽  
Search Method ◽  
Original Image ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Fractal Image ◽  
Speedup Ratio ◽  
Full Search

Fractal algorithms are used to represent similar parts of images into mathematical transforms that can recreate the original image. This chapter presents a fast fractal image compression technique via domain kick-out method, based on averaging of domain images to discard redundant domain images. It accelerates the encoding process by reducing the size of the domain pool. Results of a simulation on the proposed speedup technique on three standard test images shows that performance of the proposed technique is far superior to the present kick out methods of fractal image compression. It has reported a speedup ratio of 31.07 in average while resulting into compression ratio and retrieved image quality comparable to Jacquin's full search method.

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