Fast Fractal Image Compression Research Articles

A novel fast fractal image compression based on reinforcement learning

The concept of digital image compression is of considerable interest in the area of transmission and storage of images. The recent research in this area explores the combination of different coding techniques to achieve a better compression ratio without compromising the image quality. Fractal-based coding techniques got the attention of the research community from the very earlier days of data compression. However, those methods are computationally intensive at that time because of the exhaustive search involved to select a transformation sequence. In this paper, we propose a system that replaces the current domain range comparison in the fractal compression with a reinforcement learning technique that reduces the compression time and increases the PSNR. The system will learn from the output of the exhaustive algorithm in the initial state and discard the combinatorial search after trained on a data set. The recommended method shows a good improvement in the compression ratio, PSNR and compression time.

A novel fast fractal image compression based on reinforcement learning

The concept of digital image compression is of considerable interest in the area of transmission and storage of images. The recent research in this area explores the combination of different coding techniques to achieve a better compression ratio without compromising the image quality. Fractal-based coding techniques got the attention of the research community from the very earlier days of data compression. However, those methods are computationally intensive at that time because of the exhaustive search involved to select a transformation sequence. In this paper, we propose a system that replaces the current domain range comparison in the fractal compression with a reinforcement learning technique that reduces the compression time and increases the PSNR. The system will learn from the output of the exhaustive algorithm in the initial state and discard the combinatorial search after trained on a data set. The recommended method shows a good improvement in the compression ratio, PSNR and compression time.

A New Modified Fast Fractal Image Compression Algorithm in DCT Domain

A New Modified Fast Fractal Image Compression Algorithm in DCT Domain

Statistical feature extraction based technique for fast fractal image compression

Fractal image compression is an innovative way of image representation by using relationships among the sub-section of image itself. It utilizes the existence of self-symmetry and uses affine contractive transforms. This technique has manifold advantages like, very high compression ratio, high decompression speed, high bit-rate and resolution independence, but high computation time expenses of suitable domain search in coding phase is the major bottleneck of the technique. This paper presents a fast fractal compression scheme based on feature extraction and innovative way of image comparison. In proposed development the complexity of suitable domain search is reduced by transforming the problem from image domain to vector domain. Simulation results confirms that suggested variant leads to a faster system as compared to existing state-of art Fractal Image Compression techniques.

A Fast Fractal Image Compression Algorithm Combined with Graphic Processor Unit

Directed against the characteristics of computational intensity of fractal image compression encoding, a serial-parallel transfer mechanism is built for encoding procedures. By utilizing the properties of single instruction and multithreading execution of compute unified device architecture (CUDA), the parallel computational model of fractal encoding is built on the graphic processor unit(GPU) in order to parallelize the considerably time-consuming serial execution process of searching for the block of best match. The experimental result indicates, the algorithm in this paper shortens the encoding time to the millisecond scale and significantly boosts the execution efficiency of fractal image encoding algorithm while keeping the decoded image in good quality.

Intelligent fuzzy approach for fast fractal image compression

Abstract Fractal image compression (FIC) is recognized as a NP-hard problem, and it suffers from a high number of mean square error (MSE) computations. In this paper, a two-phase algorithm was proposed to reduce the MSE computation of FIC. In the first phase, based on edge property, range and domains are arranged. In the second one, imperialist competitive algorithm (ICA) is used according to the classified blocks. For maintaining the quality of the retrieved image and accelerating algorithm operation, we divided the solutions into two groups: developed countries and undeveloped countries. Simulations were carried out to evaluate the performance of the developed approach. Promising results thus achieved exhibit performance better than genetic algorithm (GA)-based and Full-search algorithms in terms of decreasing the number of MSE computations. The number of MSE computations was reduced by the proposed algorithm for 463 times faster compared to the Full-search algorithm, although the retrieved image quality did not have a considerable change.

A new modified fast fractal image compression algorithm

In this paper, a new fractal image compression algorithm is proposed, in which the time of the encoding process is considerably reduced. The algorithm exploits a domain pool reduction approach, along with the use of innovative predefined values for contrast scaling factor, S, instead of searching it across. Only the domain blocks with entropy greater than a threshold are considered to belong to the domain pool. The algorithm has been tested for some well-known images and the results have been compared with the state-of-the-art algorithms. The experiments show that our proposed algorithm has considerably lower encoding time than the other algorithms giving approximately the same quality for the encoded images.

An improved fast fractal image compression using spatial texture correlation

This paper utilizes a spatial texture correlation and the intelligent classification algorithm (ICA) search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.

Support Vector Machine for Fast Fractal Image Compression Base on Structure Similarity

Fractal image compression is promising both theoretically and practically. The encoding speed of the traditional full search method is a key factor rendering the fractal image compression unsuitable for real-time application. The primary objective of this paper is to investigate the comprehensive coverage of the principles and techniques of fractal image compression, and describes the implementation of a pre-processing strategy that can reduce the full searching domain blocks by training the Support Vector Machine which could recognized the self-similar pattern feature to enhance the domain block searching efficiency. In this paper, the novel image quality index (Structure Similarity, SSIM) and block property classifier based on SVM employed for the fractal image compression is investigated. Experimental results show that the scheme speeds up the encoder 15 times faster and the visual effect is better in comparison to the full search method.

A Fast Fractal Image Compression Algorithm Based on Average-Variance Function

In order to improve the efficiency and speed of match seeking in fractal compression, this paper presents an Average-Variance function which can make the optimal choice more efficiently. Based on it, we also present a fast optimal choice fractal image compression algorithm and an optimal method of constructing data tree which greatly improve the performances of the algorithm. Analysis and experimental results proved that it can improve PSNR over 1 dB and improve the coding speed over 30--40% than ordinary optimal choice algorithms such as algorithm based on center of gravity and algorithm based on variance. It can offer much higher optimal choice efficiency, higher reconstructive quality and rapid speed. It's a fast fractal encoding algorithm with high performances.

Fast fractal image compression based on one-norm of normalised block

An accelerating algorithm based on one-norm of the normalised block is proposed for fractal image coding. It can catch the best-matched block to a range block with a reduced search. Simulations show that it yields the same or superior quality as the baseline algorithm, while its runtime is shortened about 19 times.

A fast fractal image compression coding method

Fast algorithms for reducing encoding complexity of fractal image coding have recently been an important research topic. Search of the best matched domain block is the most computation intensive part of the fractal encoding process. In this paper, a fast fractal approximation coding scheme implemented on a personal computer based on matching in range block’s neighbours is presented. Experimental results show that the proposed algorithm is very simple in implementation, fast in encoding time and high in compression ratio while PSNR is almost the same as compared with Barnsley’s fractal block coding .

An FFT-based technique for fast fractal image compression

We present a new FFT-based technique of fractal image compression which can significantly speed up the process of matching domain and range blocks. In this method the isometric transformations of a domain block are chosen to permit block matching through cross correlation of range and domain blocks, which is implemented through the FFT. A suboptimal choice of domain blocks by performing a magnitude Fourier domain comparison of the range and domain blocks prior to correlation further speeds up the block matching process.