scholarly journals IMPLEMENTASI ALGORITMA FRAKTAL UNTUK KOMPRESI CITRA DENGAN METODE PENCARIAN LOKAL

2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Jatmika Jatmika ◽  
Tresia F Randongkir
Keyword(s):  
Image Compression ◽  
Local Search ◽  
Processing Time ◽  
Lossless Compression ◽  
Original Data ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Current Block ◽  
Output Time

The nature of image compression methods always fall between lossy and lossless compression. Lossy compression eliminates insignificant information while retaining the perception, while lossless compression retains the original data completely. These recent years saw the rise of Fractal Image Compression (FIC), a new lossy image compression algorithm. This algorithm features a self-similarity, which in other word it regards an image as an arrangement of copied parts of the image itself, thus we only need a composition of transformation to code an image. This paper discuss about how fractal algorithm can be applied for image compression, how Fractal Image Compression works, and how to implement it using local search where comparison is done to the nearest area (segments) only. Searching in progress often involves great amount of data which takes a considerable time. Local search can reduce the time by comparing only the nearest area within the neighbourhood of the current block, which in turn shortened the overall processing time. However, the sharply reduced processing time achieved by localizing the search does not drastically reduce the quality of the output time.

Improvement of Fractal Image Compression Coding Based on Quadtree

2012 ◽  
Vol 532-533 ◽  
pp. 1157-1161
Author(s):  
Hong Tao Hu ◽  
Qi Fei Liu
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Original Image ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Global Matching ◽  
Domain Block ◽  
Compression Coding

The goal of image compression is to represent an image with as few number of bits as possible while keeping the quality of the original image. With the characteristics of higher compression ratio, fractal image coding has received much attention recently. However, conventional fractal compression approach needs more time to code the original image. In order to overcome the time-consuming issue, a Quadtree-based partitioning and matching scheme is proposed. During the partitioning phase, an image frame is partitioned into tree-structural segments. And during a matching phase, a rang block only searches its corresponding domain block around previous matched domain block. Such local matching procedures will not stop until a predefined matching threshold is obtained. The preliminary experimental results show that such sub-matching rather than a global matching scheme dramatically decreases the matching complexity, while preserving the quality of an approximate image to the original after decoding process. In particular, the proposed scheme improves the coding process up to 2 times against the conventional fractal image coding approach.

scholarly journals PERFORMANCE EVALUATION OF IMAGE QUALITY BASED ON FRACTAL IMAGE COMPRESSION

2012 ◽  
Vol 2 (1) ◽  
pp. 20-27
Author(s):  
Gaganpreet Kaur ◽  
Hitashi Hitashi ◽  
Dr. Gurdev Singh
Keyword(s):  
Fixed Point ◽  
Performance Evaluation ◽  
Image Compression ◽  
Fractal Image Compression ◽  
Still Images ◽  
Coding Schemes ◽  
Practical Algorithms ◽  
Fractal Image ◽  
Spatial Frequencies

Fractal techniques for image compression haverecently attracted a great deal of attention. Fractalimage compression is a relatively recenttechnique based on the representation of animage by a contractive transform, on the space ofimages, for which the fixed point is close to theoriginal image. This broad principle encompassesa very wide variety of coding schemes, many ofwhich have been explored in the rapidly growingbody of published research.Unfortunately, littlein the way of practical algorithms or techniqueshas been published. Here present a technique forimage compression that is based on a very simpletype of iterative fractal. In our algorithm awavelet transform (quadrature mirror filterpyramid) is used to decompose an image intobands containing information from differentscales (spatial frequencies) and orientations. Theconditional probabilities between these differentscale bands are then determined, and used as thebasis for a predictive coder.We undertake a study of the performance offractal image compression. This paper focusesimportant features of compression of still images,including the extent to which the quality of imageis degraded by the process of compression anddecompression.The numerical experiment is doneby considering various types of images and byapplying fractal Image compression to compressan image. It was found that fractal yields betterresult as compared to other compressiontechniques. It provide better peak signal to noiseratio as compare to other techniques, but it takehigher encoding time.The numerical results arecalculated in Matlab.

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