Fast fractal image compression using spatial correlation

Truong, Trieu‐Kien; Kung, Chih‐Ming; Jeng, Jyh-Horng; Hsieh, Meng‐Hsuan

doi:10.1016/j.chaos.2004.03.015

Cited by 68 publications

(36 citation statements)

References 5 publications

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“…Fan and Liu [11] presented the matching algorithm based on the Standard Deviation (STD) between range blocks and domain blocks. Wang et al [12] proposed Correlation information feature to find nearest neighbor domain block for each range block.…”

Section: Related Work On Fractal Image Codingmentioning

confidence: 99%

“…Compression ratio (CR) = (11) Therefore, the compression ratio in percentage is computed from (11) and given by equation (12).…”

Section: Quality Measurementioning

confidence: 99%

“…Compression ratio (CR) in % = ( ) (12) When measuring the quality of the compressed video, the peak signal-to-noise ratio is used. Sometimes mean squared error (mse) is also used, which is given by equation (13).…”

Section: Quality Measurementioning

confidence: 99%

See 2 more Smart Citations

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

Kamble¹,

Thakur²,

Bajaj³

2016

IJIMAI

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---Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression.

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Section: Related Work On Fractal Image Codingmentioning

confidence: 99%

“…Compression ratio (CR) = (11) Therefore, the compression ratio in percentage is computed from (11) and given by equation (12).…”

Section: Quality Measurementioning

confidence: 99%

See 1 more Smart Citation

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

Kamble¹,

Thakur²,

Bajaj³

2016

IJIMAI

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show abstract

“…Other approaches produce improvements of FIC by tree structure search methods [13,14], parallel search methods [15,16] or by using two domain pools in two steps of FIC (AP2D) [17]. Also, the spatial correlation in both the domain pool and the range pool is added to improve FIC as developed by Truong et al [18]. In these methods, high speedup factors are often associated with some loss of reconstructed image quality.…”

Section: Introductionmentioning

confidence: 99%

A reduced domain pool based on DCT for a fast fractal image encoding

Douda

Bagri

Imrani

2011

ELCVIA

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Fractal image compression is time consuming due to the search of the matching between range and domain blocks. In order to improve this compression method, we propose firstly, a fast method for reducing the computational complexity of fractal encoding by reducing the size of the domain pool. This reduction is based on the lowest horizontal and vertical DCT coefficients of domain blocks. The experimental results on the test images show that the proposed method reduces the time computation and reaches a high speedup factor without decreasing the image quality. Secondly, we combine our method to the AP2D approach which uses two domain pools in two steps of encoding. A more reduction of encoding time is obtained without decreasing the image quality.

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“…In [6][7] authors proposed classification methods base on the feature of domain blocks. [8] Proposed a kind of neighborhood matching method based on spatial correlation which makes use of the information of matched range blocks and effectively reduced the encoding time. Quantum Evolutionary Algorithms are novel algorithms using probabilistic representation for possible solutions.…”

Section: Introductionmentioning

confidence: 99%

Using Triangular Function To Improve Size Of Population In Quantum Evolution Algorithm For Fractal Image Compression

Qorbani¹,

Nodehi²,

Nodehi³

2011

ACIJ

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Fractal Image Compression is a well-known problem which is in the class of NP-Hard problems. Quantum Evolutionary Algorithm is a novel optimization algorithm which uses a probabilistic representation for solutions and is highly suitable for combinatorial problems like Knapsack problem. Genetic algorithms are widely used for fractal image compression problems, but QEA is not used for this kind of problems yet. This paper improves QEA whit change population size and used it in fractal image compression. Utilizing the self-similarity property of a natural image, the partitioned iterated function system (PIFS) will be found to encode an image through Quantum Evolutionary Algorithm (QEA) methodExperimental results show that our method has a better performance than GA and conventional fractal image compression algorithms.

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Fast fractal image compression using spatial correlation

Cited by 68 publications

References 5 publications

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

A reduced domain pool based on DCT for a fast fractal image encoding

Using Triangular Function To Improve Size Of Population In Quantum Evolution Algorithm For Fractal Image Compression

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