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Relevant bibliographies by topics / Fractal image coding

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Journal articles

Academic literature on the topic 'Fractal image coding'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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Journal articles on the topic "Fractal image coding"

1

LIAN, SHIGUO, XI CHEN, and DENGPAN YE. "SECURE FRACTAL IMAGE CODING BASED ON FRACTAL PARAMETER ENCRYPTION." Fractals 17, no. 02 (2009): 149–60. http://dx.doi.org/10.1142/s0218348x09004405.

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In recent work, various fractal image coding methods are reported, which adopt the self-similarity of images to compress the size of images. However, till now, no solutions for the security of fractal encoded images have been provided. In this paper, a secure fractal image coding scheme is proposed and evaluated, which encrypts some of the fractal parameters during fractal encoding, and thus, produces the encrypted and encoded image. The encrypted image can only be recovered by the correct key. To maintain security and efficiency, only the suitable parameters are selected and encrypted through

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2

Barthel, Kai Uwe. "Entropy Constrained Fractal Image Coding." Fractals 05, supp01 (1997): 17–26. http://dx.doi.org/10.1142/s0218348x97000607.

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In this paper we present an entropy constrained fractal coding scheme. In order to get high compression rates, previous fractal coders used hierarchical coding schemes with variable range block sizes. Our scheme uses constant range block sizes, but the complexity of the fractal transformations is adapted to the image contents. The entropy of the fractal code can be significantly reduced by introducing geometrical codebooks of variable size and a variable order luminance transformation. We propose a luminance transformation consisting of a unification of fractal and transform coding. With this

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3

LU, JIAN, JIAPENG TIAN, CHEN XU, and YURU ZOU. "A DICTIONARY LEARNING APPROACH FOR FRACTAL IMAGE CODING." Fractals 27, no. 02 (2019): 1950020. http://dx.doi.org/10.1142/s0218348x19500208.

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In recent years, sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, this paper investigates incorporating a dictionary learning approach into fractal image coding, which leads to a new model containing three terms: a patch-based sparse representation prior over a learned dictionary, a quadratic term measuring the closeness of the underlying image to a fractal image, and a data-fidelity term capturing the statistics of Gaussian noise. After the dictionary is learned, the resulting optimization problem with fractal coding can be solved

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4

Chen, Yuanxu, Yupin Luo, and Dongcheng Hu. "Image superresolution using fractal coding." Optical Engineering 47, no. 1 (2008): 017007. http://dx.doi.org/10.1117/1.2835453.

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5

Jacquin, A. E. "Fractal image coding: a review." Proceedings of the IEEE 81, no. 10 (1993): 1451–65. http://dx.doi.org/10.1109/5.241507.

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6

Ida, T., and Y. Sambonsugi. "Image segmentation using fractal coding." IEEE Transactions on Circuits and Systems for Video Technology 5, no. 6 (1995): 567–70. http://dx.doi.org/10.1109/76.477072.

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7

Khaitu, Shree Ram, and Sanjeeb Prasad Panday. "Fractal Image Compression Using Canonical Huffman Coding." Journal of the Institute of Engineering 15, no. 1 (2020): 91–105. http://dx.doi.org/10.3126/jie.v15i1.27718.

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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 symbol

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8

YUEN, CHING-HUNG, and KWOK-WO WONG. "CRYPTANALYSIS ON SECURE FRACTAL IMAGE CODING BASED ON FRACTAL PARAMETER ENCRYPTION." Fractals 20, no. 01 (2012): 41–51. http://dx.doi.org/10.1142/s0218348x12500041.

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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 suc

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9

Dalui, Indrani, SurajitGoon, and Avisek Chatterjee. "A NEW APPROACH OF FRACTAL COMPRESSION USING COLOR IMAGE." International Journal of Engineering Technologies and Management Research 6, no. 6 (2020): 74–71. http://dx.doi.org/10.29121/ijetmr.v6.i6.2019.395.

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Fractal image compression depends on self-similarity, where one segment of a image is like the other one segment of a similar picture. Fractal coding is constantly connected to grey level images. The simplest technique to encode a color image by gray- scale fractal image coding algorithm is to part the RGB color image into three Channels - red, green and blue, and compress them independently by regarding each color segment as a specific gray-scale image. The colorimetric association of RGB color pictures is examined through the calculation of the relationship essential of their three-dimension

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10

Götting, Detlef, Achim Ibenthal, and Rolf-Rainer Grigat. "Fractal Image Coding and Magnification Using Invariant Features." Fractals 05, supp01 (1997): 65–74. http://dx.doi.org/10.1142/s0218348x97000644.

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Fractal image coding has significant potential for the compression of still and moving images and also for scaling up images. The objective of our investigations was twofold. First, compression ratios of factor 60 and more for still images have been achieved, yielding a better quality of the decoded picture material than standard methods like JPEG. Second, image enlargement up to factors of 16 per dimension has been realized by means of fractal zoom, leading to natural and sharp representation of the scaled image content. Quality improvements were achieved due to the introduction of an extende

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