Adaptive fractal image compression using PSO

Muruganandham, A.; Banu, R. S. D. Wahida

doi:10.1016/j.procs.2010.11.044

Cited by 29 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the proposed method employs one of the bio-inspired algorithms, the image compression techniques based on optimization algorithms are reviewed and the comparison is tabulated in Table 2. A fast fractal encoding technique using PSO algorithm is presented in [19], to decrease the amount of time required for the encoding process. The usage of PSO algorithm increases the fractal encoding speed and also conserves the image quality.…”

Section: Related Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

Preethi,

Loganathan

2019

IJEAT

View full text Add to dashboard Cite

In the recent days, the importance of image compression techniques is exponentially increased due to the generation of massive amount of data which needs to be stored or transmitted. Numerous approaches have been presented for effective image compression by the principle of representing images in its compact form through the avoidance of unnecessary pixels. Vector quantization (VA) is an effective method in image compression and the construction of quantization table is an important process is an important task. The compression performance and the quality of reconstructed data are based on the quantization table, which is actually a matrix of 64 integers. The quantization table selection is a complex combinatorial problem which can be resolved by the evolutionary algorithms (EA). Presently, EA became famous to resolve the real world problems in a reasonable amount of time. This chapter introduces Firefly (FF) with Teaching and learning based optimization (TLBO) algorithm termed as FF-TLBO algorithm for the selection of quantization table and introduces Firefly with Tumbling algorithm termed as FF-Tumbling algorithm for the selection of search space. As the FF algorithm faces a problem when brighter FFs are insignificant, the TLBO algorithm is integrated to it to resolve the problem and Tumbling efficiently train the algorithm to explore all direction in the solution space. This algorithm determines the best fit value for every bock as local best and best fitness value for the entire image is considered as global best. When these values are found by FF algorithm, compression process takes place by efficient image compression algorithm like Run Length Encoding and Huffman coding. The proposed FF-TLBO and FF-Tumbling algorithm is evaluated by comparing its results with existing FF algorithm using a same set of benchmark images in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR). The obtained results ensure the superior performance of FF-TLBO and FF-Tumbling algorithm over FF algorithm and make it highly useful for real time applications.

show abstract

Section: Related Studiesmentioning

confidence: 99%

“…The solution will be updated using the differences between the present and new mean as given in Eq. (19).…”

Section: B Ff-tlbo Algorithmmentioning

confidence: 99%

Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

Preethi,

Loganathan

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…New methods for fractal image compression using PSO were discussed in Refs. and . PSO reduces the search space for finding the self similarities in the given image.…”

Section: Related Backgroundmentioning

confidence: 99%

A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Sophia

Anitha

2017

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques. K E Y W O R D Scontextual compression, contourlet transform, magnetic resonance imaging brain image, optimized prediction, wavelet transform Int.

show abstract

“…Adaptive fractal compression method [4] uses Particle Swam Optimization (PSO) for searching. Here, searching time is reduced and hence, encoding time of fractal image compression, but with 1.2 dB loss in image quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Fractal based Image Compression Techniques

Kadam¹,

Rathod²

2017

IJCA

View full text Add to dashboard Cite

Fractal image compression offers high compression ratios and quality image reconstruction. It uses various techniques as the fractal with DCT, wavelet, neural network, genetic algorithms, quantum acceleration etc. Additionally, because fractals are infinitely magnifiable, fractal compression is resolution independent and so a single compressed image can be used efficiently for display in any image resolution including resolution higher than the resolution of the original image. Breaking an image into pieces and identifying selfsimilar ones is the main principle of the approach. In this paper, the different issues in fractal image compression as partitioning, larger encoding time, compression ratio, quality of the reconstructed image, decoding time, SSIM(Structured Similarity Index) are discussed and highlighted. The various areas for improvement as larger encoding time and PSNR are also suggested. The various parameters for evaluating the performance of these techniques as PSNR, compression ratio, encoding time, and decoding time are also suggested. Comparison of Fractal techniques for color image, texture and satellite image is done using different parameters as compression time, compression ratio and PSNR. The hybrid method which combines Fractal quad tree with wavelet and Huffman coding is implemented and compared different parameters as compression ratio and the compression time of the proposed method with the existing methods.

show abstract

Adaptive fractal image compression using PSO

Cited by 29 publications

References 11 publications

Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

Modified Firefly Algorithm for Vector Quantization Codebook Design in Image Compression

A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Fractal based Image Compression Techniques

Contact Info

Product

Resources

About