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.

Modified No Search Scheme based Domain Blocks Sorting Strategies for Fractal Image Coding

2014 ◽  
Vol 69 (10-11) ◽  
pp. 511-520
Author(s):  
Xing-Yuan Wang ◽  
Dou-Dou Zhang ◽  
Na Wei
Keyword(s):  
Mean Square Error ◽  
Image Coding ◽  
The Other ◽  
Mean Square ◽  
Minimum Value ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Domain Block ◽  
Partition Method

AbstractA novel fractal image coding algorithm based on domain blocks sorting strategies and modified no search scheme is proposed in this paper. On one hand, in order to improve the encoding time, a modified no search (MNS) scheme is adopted. Firstly, the image is divided into blocks of different size utilizing an adaptive quadtree partition method. Secondly, one finds the location of the best matching domain block using the MNS scheme for the range blocks, whose sizes are larger than the preset minimum value. Thirdly, the types of the range block and domain block are computed employing the proposed approach, and then the corresponding computation of mean square error (MSE) is determined. The computation of the MSE is reduced and the encoding phase speeds up. On the other hand, the range blocks with the minimal sizes are encoded applying the proposed domain blocks sorting (DBS) method. Contrast experiment results show that the proposed algorithm can obtain good quality of the reconstructed images and shorten the encoding time significantly.

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.

AN IMPROVED FRACTAL IMAGE CODING METHOD

Fractals ◽  
2009 ◽  
Vol 17 (04) ◽  
pp. 451-457 ◽  
Author(s):  
XING-YUAN WANG ◽  
FAN-PING LI ◽  
ZHI-FENG CHEN
Keyword(s):  
Image Coding ◽  
Nearest Neighbor ◽  
Optimal Solution ◽  
Search Method ◽  
Neighbor Search ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Coding Method ◽  
Domain Block

This paper presents a fast fractal image coding method based on quadtree division, improved neighbor search and asymptotic strategy. We search the optimal matched domain block of a range block in its five nearest neighbor blocks and make asymptotic moves along the direction of potential optimal solution. If the optimal solution can not be improved, we carry out quadtree division for this range block until it caters to our demand or reaches greatest division level. The experimental results show that the coding speed of the proposed method declined slightly, but it has a better quality of reconstructed image and higher compression ratio in comparisons with no search method.

An Adaptive Partition for Fractal Image Coding

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 243-256 ◽  
Author(s):  
Franck Davoine ◽  
Etienne Bertin ◽  
Jean-Marc Chassery
Keyword(s):  
Compression Ratio ◽  
Image Coding ◽  
Visual Quality ◽  
Second Step ◽  
Affine Transformations ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Delaunay Triangles

In this paper we present a flexible partitioning scheme for fractal image compression, based on the Delaunay triangles. The aim is to have the advantage of triangular blocks over squares, in terms of adaptivity to the image content. In a first step, the triangulation is computed so that the triangles are more densely distributed in regions containing interesting features such as corners and edges, or so that they tend to run along the strong edges in the image. In a second step we merge adjacent triangles into quadrilaterals, in order to decrease the number of blocks. Quadrilaterals permit a reduction of the number of local contractive affine transformations composing the fractal transform, and thus to increase the compression ratio, while preserving the visual quality of the decoded image.

CRYPTANALYSIS ON SECURE FRACTAL IMAGE CODING BASED ON FRACTAL PARAMETER ENCRYPTION

Fractals ◽  
2012 ◽  
Vol 20 (01) ◽  
pp. 41-51 ◽  
Author(s):  
CHING-HUNG YUEN ◽  
KWOK-WO WONG
Keyword(s):  
Image Coding ◽  
Standard Test ◽  
Scaling Factor ◽  
Depth Information ◽  
Selective Encryption ◽  
Fractal Parameter ◽  
Fractal Image ◽  
Fractal Image Coding ◽  
Plain Image ◽  
Domain Block

The vulnerabilities of the selective encryption scheme for fractal image coding proposed by Lian et al.1 are identified. By comparing multiple cipher-images of the same plain-image encrypted with different keys, the positions of unencrypted parameters in each encoded block are located. This allows the adversary to recover the encrypted depth of the quadtree by observing the length of each matched domain block. With this depth information and the unencrypted parameters, the adversary is able to reconstruct an intelligent image. Experimental results show that some standard test images can be successfully decoded and recognized by replacing the encrypted contrast scaling factor and brightness offset with specific values. Some remedial approaches are suggested to enhance the security of the scheme.

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.

scholarly journals Fractal Image Coding for Improve the Quality of Medical Images

2014 ◽  
Vol 8 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Jaehong Park ◽  
Cheolwoo Park ◽  
Wonseok Yang
Keyword(s):  
Image Coding ◽  
Medical Images ◽  
Fractal Image ◽  
Fractal Image Coding
Sign in / Sign up

Export Citation Format

Share Document