Efficient Domain Search for Fractal Image Compression Using Feature Extraction Technique

2012 ◽  
Vol 488-489 ◽  
pp. 1587-1591
Author(s):  
Amol G. Baviskar ◽  
S. S. Pawale
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Low Complexity ◽  
Contractive Mapping ◽  
Fractal Image Compression ◽  
Compression Technique ◽  
Image Encoding ◽  
Fractal Image ◽  
Coding Method ◽  
The Given

Fractal image compression is a lossy compression technique developed in the early 1990s. It makes use of the local self-similarity property existing in an image and finds a contractive mapping affine transformation (fractal transform) T, such that the fixed point of T is close to the given image in a suitable metric. It has generated much interest due to its promise of high compression ratios with good decompression quality. Image encoding based on fractal block-coding method relies on assumption that image redundancy can be efficiently exploited through block-self transformability. It has shown promise in producing high fidelity, resolution independent images. The low complexity of decoding process also suggested use in real time applications. The high encoding time, in combination with patents on technology have unfortunately discouraged results. In this paper, we have proposed efficient domain search technique using feature extraction for the encoding of fractal image which reduces encoding-decoding time and proposed technique improves quality of compressed image.

THE PARALLEL-PIPELINED IMPLEMENTATION OF THE FRACTAL IMAGE COMPRESSION FOR RECONFIGURABLE COMPUTING SYSTEMS

2021 ◽  
pp. 37-44
Author(s):  
I. I. Levin ◽  
M. D. Chekina
Keyword(s):  
Image Compression ◽  
Reconfigurable Computing ◽  
Fractal Image Compression ◽  
Computing Systems ◽  
Image Encoding ◽  
Fractal Image ◽  
Computational Structure ◽  
Parallel Pipeline ◽  
Performance Reduction ◽  
Critical Resource

The developed fractal image compression method, implemented for reconfigurable computing systems is described. The main idea parallel fractal image compression based on parallel execution pairwise comparison of domain and rank blocks. Achievement high performance occurs at the expense of simultaneously comparing maximum number of pairs. Implementation fractal image compression for reconfigurable computing systems has two critical resources, as number of input channels and FPGA Look-up Table (LUT). The main critical resource for fractal image compression is data channels, and implementation this task for reconfigurable computing systems requires parallel-pipeline computations organization replace parallel, preliminarily produced performance reduction parallel computational structure. The main critical resource for fractal image compression is data channels, and implementation this task for reconfigurable computing systems requires parallel-pipeline computations organization replace parallel computations organiation. For using parallel-pipeline computations organization, preliminarily have produce performance reduction parallel computational structure. Each operator has routed to computational structure sequentially (bit by bit) to save computational resources and reduces equipment downtime. Storing iterated functions system coefficients for image encoding has been introduced in data structure, which correlates between corresponding parameters the numbers of rank and domain blocks. Applying this approach for parallel-pipeline programs allows scaling computing structure to plurality programmable logic arrays (FPGAs). Task implementation on the reconfigurable computer system Tertius-2 containing eight FPGAs 15 000 times provides performed acceleration relatively with universal multi-core processor, and 18 – 25 times whit to existing solutions for FPGAs.

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 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.  

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.

Sign in / Sign up

Export Citation Format

Share Document