Genetic algorithm based on discrete wavelet transformation for fractal image compression

Ming-sheng, WU

doi:10.1016/j.jvcir.2014.09.001

Cited by 38 publications

(19 citation statements)

References 19 publications

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“…In comparison, between fractal image and audio coding, there have been many articles published on fractal image coding [18,19,21,[30][31][32] while there is less on Fractal Audio Coding as presented above. Further research is required in the area of Fractal Audio Coding in terms of encoding time, compressed audio quality and compression ratio in order to effectively use with the audio files and stand side by side with other audio compression techniques.…”

Section: Related Workmentioning

confidence: 99%

Investigating the Effect of Block Length on the Performance of Fractal Coding Using Audio Files

Ali

George

Saleh

et al. 2020

Period. Polytech. Elec. Eng. Comp. Sci.

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The goal of compression techniques is to reducing the size of data and decreasing the communication cost while transferring data. Fractal based coding technique is widely used to compress images files which provides high compression ratio and good image quality. However, like a compression technique, it is still limited because of the difference of the human perceptions between audio and image files, the long time for searching the best possible domain blocks and many comparisons in the encoding process. For those reasons, Fractal Coding had not broadly studied on audio data. Few years ago, Fractal Coding has been extended to apply on the audio data. In this paper, the application of the Fractal Coding on different types of audio files is investigated. Moreover, the effect of block length on the audio quality and compression performance are highlighted since block length is considered the main factor in the Fractal Coding algorithm. A GTZAN dataset is adopted in the evaluation and the experimental results show that there is an inverse relationship between block length and audio quality and proportional relationship between block length and compression ratio and factor. Furthermore, it can be noticed that the Fractal Coding can be compressed any speech and music audio signal directly with acceptable quality, PSNR 39 dB on average with a high compression ratio around 90 % with compression factor around 10 when the block length is 20 samples.

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Section: Related Workmentioning

confidence: 99%

Investigating the Effect of Block Length on the Performance of Fractal Coding Using Audio Files

Ali

George

Saleh

et al. 2020

Period. Polytech. Elec. Eng. Comp. Sci.

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“…Genetic algorithm based on discrete wavelet transformation for fractal image compression [17] is implemented to overcome drawbacks of time-consuming fractal coder. The proposed method is 100 times faster than the full search method.…”

Section: Literature Reviewmentioning

confidence: 99%

Fractal based Image Compression Techniques

Kadam¹,

Rathod²

2017

IJCA

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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.

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“…First, a FIC (Fractal Image compression) using DWT is proposed to ignore unnecessary MSE computations produced by Dihedral transformations. The proposed Generic algorithm is 100 times faster than the full search method with the penalty of 1.13 dB decay at the retrieved image quality [25].…”

Section: Literature Reviewmentioning

confidence: 99%

A Review: DWT-DCT Technique and Arithmetic-Huffman Coding based Image Compression

Kumar¹,

Brar²,

Kumar³

et al. 2015

IJEM

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Nowadays, the volume of the data is increasing with time which generates a problem in storage and transfer. To overcome this problem, the data compression is the only solution. Data compression is the science (or an art) of representing information in compact form. This is an active research area. Compression is to save the hardware storage space and transmission bandwidth by reducing the redundant bits. Basically, lossless & lossy are two types of data compression technique. In lossless data compression, original data is similar to decompressed or decoded data, but in lossy technique is not same. In this paper, Study lossless image compression technique. The purpose of image compression is to maximum bandwidth utilization and reduces storage capacity. This technique is beneficial to image storage and transfer. At the present time, Mostly image compression research have focused on the wavelet transform due to better performance over another transform. The performance is evaluated by using MSE & PSNR. DWT, quantization, Arithmetic, Huffman coding and DCT techniques are briefly introduced. After decompression, the quality of image is evaluated using PSNR parameter between original & decoded image. Compression ratio (CR) parameter is calculated to measure how many times image compressed.

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Genetic algorithm based on discrete wavelet transformation for fractal image compression

Cited by 38 publications

References 19 publications

Investigating the Effect of Block Length on the Performance of Fractal Coding Using Audio Files

Investigating the Effect of Block Length on the Performance of Fractal Coding Using Audio Files

Fractal based Image Compression Techniques

A Review: DWT-DCT Technique and Arithmetic-Huffman Coding based Image Compression

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