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.

IMPROVED FRACTAL IMAGE COMPRESSION BASED ON ROBUST FEATURE DESCRIPTORS

2011 ◽  
Vol 11 (04) ◽  
pp. 571-587 ◽  
Author(s):  
WILLIAM ROBSON SCHWARTZ ◽  
HELIO PEDRINI
Keyword(s):  
Image Compression ◽  
Computational Cost ◽  
Computational Time ◽  
Natural Scenes ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Feature Descriptors ◽  
High Compression ◽  
Speed Up ◽  
Computationally Intensive

Fractal image compression is one of the most promising techniques for image compression due to advantages such as resolution independence and fast decompression. It exploits the fact that natural scenes present self-similarity to remove redundancy and obtain high compression rates with smaller quality degradation compared to traditional compression methods. The main drawback of fractal compression is its computationally intensive encoding process, due to the need for searching regions with high similarity in the image. Several approaches have been developed to reduce the computational cost to locate similar regions. In this work, we propose a method based on robust feature descriptors to speed up the encoding time. The use of robust features provides more discriminative and representative information for regions of the image. When the regions are better represented, the search for similar parts of the image can be reduced to focus only on the most likely matching candidates, which leads to reduction on the computational time. Our experimental results show that the use of robust feature descriptors reduces the encoding time while keeping high compression rates and reconstruction quality.

scholarly journals Exploiting Data-Parallelism on Multicore and SMT Systems for Implementing the Fractal Image Compressing Problem

2016 ◽  
Vol 10 (1) ◽  
pp. 34
Author(s):  
Rodrigo da Rosa Righi ◽  
Vinicius F. Rodrigues ◽  
Cristiano A. Costa ◽  
Roberto Q. Gomes
Keyword(s):  
Image Compression ◽  
Fractal Theory ◽  
System Level ◽  
Data Parallelism ◽  
Compression Method ◽  
Fractal Compression ◽  
Fractal Image ◽  
Coding Method ◽  
Slow Growing ◽  
Dual Core

This paper presents a parallel modeling of a lossy image compression method based on the fractal theory and its evaluation over two versions of dual-core processors: with and without simultaneous multithreading (SMT) support. The idea is to observe the speedup on both configurations when changing application parameters and the number of threads at operating system level. Our target application is particularly relevant in the Big Data era. Huge amounts of data often need to be sent over low/medium bandwidth networks, and/or to be saved on devices with limited store capacity, motivating efficient image compression. Especially, the fractal compression presents a CPU-bound coding method known for offering higher indexes of file reduction through highly time-consuming calculus. The structure of the problem allowed us to explore data-parallelism by implementing an embarrassingly parallel version of the algorithm. Despite its simplicity, our modeling is useful for fully exploiting and evaluating the considered architectures. When comparing performance in both processors, the results demonstrated that the SMT-based one presented gains up to 29%. Moreover, they emphasized that a large number of threads does not always represent a reduction in application time. In average, the results showed a curve in which a strong time reduction is achieved when working with 4 and 8 threads when evaluating pure and SMT dual-core processors, respectively. The trend concerns a slow growing of the execution time when enlarging the number of threads due to both task granularity and threads management.

Multiresolution Analysis of Fractal Image Compression

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 215-229
Author(s):  
Gregory Caso ◽  
C.-C. Jay Kuo
Keyword(s):  
Wavelet Transform ◽  
Image Compression ◽  
Domain Structure ◽  
Multiresolution Analysis ◽  
Transform Coding ◽  
Transform Domain ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Affine Mappings ◽  
Encoding Method

In this research, we perform a multiresolution analysis of the mappings used in fractal image compression. We derive the transform-domain structure of the mappings and demonstrate a close connection between fractal image compression and wavelet transform coding using the Haar basis. We show that under certain conditions, the mappings correspond to a hierarchy of affine mappings between the subbands of the transformed image. Our analysis provides new insights into the mechanism underlying fractal image compression, leads to a new non-iterative transform-domain decoding algorithm, and suggests a new transform-domain encoding method with extensions to wavelets other than the Haar transform.

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