Fractal image compression with region-based functionality

IEEE Transactions on Image Processing, 1999

Fractal image compression is a relatively recent technique based on the representation of an image by a contractive transform, on the space of images, for which the fixed point is close to the original image. This broad principle encompasses a very wide variety of coding schemes, many of which have been explored in the rapidly growing body of published research. While certain theoretical aspects of this representation are well established, relatively little attention has been given to the construction of a coherent underlying image model which would justify its use. Most purely fractalbased schemes are not competitive with the current state of the art, but hybrid schemes incorporating fractal compression and alternative techniques have achieved considerably greater success. This review represents a survey of the most significant advances, both practical and theoretical, since the publication in 1990 of Jacquin's original fractal coding scheme.

1997

Abstract A fast image compression technique as well as its progressive image transmission (PIT) version using fractals is presented which uses a small pool of domains extracted using visually significant patterns. The affine transformations for an edge block are obtained by using its edge characteristics instead of the minimum mean square error criterion.

Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), 1998

The paper introduces a new method based on deterministic search to fractal image compression. In order to find a good region-based partitioning, we propose a deterministic search method for finding the blocks to be merged. For each range, a list of best N domains is maintained. When two ranges are to be merged and their common edge disappears, for the new range the best N domains are selected only from the 2×N domain extension of the two ranges. At each step the edge with minimum collage error increase is deterministically selected and the two corresponding ranges are merged. The process starts with atomic blocks as ranges and ends when the desired number of ranges is achieved. In order to reduce the encoding time, a suboptimal initialization method is also considered. Experimental results prove that our method yields a better rate-distortion curve than the classic quad-tree partitioning scheme.

Proceedings of the IEEE, 1993

Specifrc classes of Fractals can be used for Di-1 Image Compression or Coding. For such an application, the general problem statement is the following. Given any original discrete image specified by an array of pixels, how can a computer construct a fractal imagMhe coded image-which is both visually close to the original one, and has a digital representation which requires fewer bits than the original image. In this paper, we describe an approach to image coding based on a fractal theory of iterated contractive transformations defined piecewise. The main characteristics of this approach are that: i) it relies on the assumption that image redundancy can be eftciently captured and exploited through piecewise serf-transformability on a block-wise basis, and ii) it approximates an original image by a jhctal image, obtained from a finite number of iterations of an image transformation called a fractal code. We refer to this approach as Fractal B k k Coding. The general coding-decoding system is based on the construction, for an original image to encode, of a fractal C O & contractive image transformation for which the original image is an approximate jixed poin.-which, when applied iteratively on any initial image at the decoder, produces a sequence of images which converges to a fractal approximation of the original. The fractal code consists of a description of both an image partition and a contractive image tran$ormation defined as a list of parent and child block transformations, each specified by a small set of quantized parameters. We describe the design of such a system for the encoding of monochrome digital images at rates below 1 blpixel, without any entropy coding of the parameters of the fractal code. We also present novel ideas and extensions from the work of a number of researchers which has appeared since the publication of the fractal block coding work of the author.

Our proposed approach presents a method to reduce the encoding time called Fast Algorithm of Fractal Image Compression based on Entropy Values (FAFICEVs). This technique will be reducing the size of the domain pool. FFICEVs technique is based on the observation that many blocks domain are never used in a typical fractal encoding, and only a fraction of this large domain pool is actually used in the fractal coding. The collection of used domain blocks is localized in regions with high degree of structure. This technique focuses on the implementation issues and presents the first empirical experiments analyzing the performance of benefits of entropy approach to fractal image compression. The experiment is carried out with technique of quadtree partitioning, allowing up to three (4x8, 4x16, and 8x16 pixel) is examined. The new algorithm gives a good quality of reconstructed image with speeding in encoding time.

Proceedings 20th Eurographics UK Conference, 2002

accelerate fractal image compression times at a noticeable extent.

2008 International Conference on Computer Engineering & Systems, 2008

This paper proposes a simplified fractal image compression algorithm which is implemented on a block by block basis. This algorithm achieves a compression ratio of up to 10 with a peak signal to noise ratio (PSNR) as high as 35dB. The idea of the proposed algorithm is based on the segmentation of the image, first, into blocks to setup reference blocks. The image is then decomposed again into block ranges and a search process is carried out to find the reference blocks with best match. The transmitted or stored values, after compression, are the reference block values and the indices of the reference block that achieves the best match. If there is no match, the average value of the block range is transmitted or stored instead. The advantages of the proposed algorithm are the simplicity of computation and the high PSNR achieved.

Journal of Multimedia, 2007

A novel object-based fractal monocular and stereo video compression scheme with quadtree-based motion and disparity compensation is proposed in this paper. Fractal coding is adopted and each object is encoded independently by a prior image segmentation alpha plane, which is defined exactly as in MPEG-4. The first n frames of right video sequence are encoded by using the Circular Prediction Mapping (CPM) and the remaining frames are encoded by using the Non Contractive Interframe Mapping (NCIM). The CPM and NCIM methods accomplish the motion estimation/compensation of right video sequence. According to the different coding or user requirements, the spatial correlations between the left and right frames can be explored by partial or full affine transformation quadtree-based disparity estimation/ compensation, or simply by applying CPM/NCIM on left video sequence.

Now a days saving the bandwidth on the Internet is a major issue. In Indian scenario the issue is more relevant because we don't have very high speed lines to handle the huge traffic. In this era we cannot think of the messages without still images and videos. Videos are considered as the sequences of frames or images. Therefore image is the basic unit of compression in multimedia messages. Images can be broadly classified into two classes. One that cannot tolerate any loss like technical drawings, geometric shapes or medical images. Another class of image can tolerate loss upto certain extent until the loss is noticible by human eye. The natural images lie in the later class. This paper focuses on lossy compression of natural images with fractal approach. The objective of the paper is two fold. Firstly the search area is minimized by consturcting subsets of domain and range block sets and restricting the search in that particular subset only, this results in fast search. Another objective is to obtain desired closeness between range and domain blocks by adjusting the closeness parameter defined in the paper. To achieve the lossy compression with fractals the image is partitioned into many square shaped blocks called domain blocks. After that the further partition into smaller blocks called range blocks is carried out. For each range block the best matched domain block is searched in the entire image. The performance of the matching procedure is examined by the fact that how closely the matching is done.

Various types of compression methods have been proposed using different techniques to achieve high compression ratio and high quality of reconstructed image in low computational time. One of these methods is fractal image compression. Fractal technique is based on the self-similarities in the image .