Image and audio fractal compression: comprehensive review, enhancements and research directions

Ali, Ahmed Huaasin; Abbas, Ali; George, Loay E.; Mokhtar, Mohd Rosmadi

doi:10.11591/ijeecs.v15.i3.pp1564-1570

Cited by 10 publications

(5 citation statements)

References 22 publications

(31 reference statements)

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“…At the end, each range block is encoded by four parameters called Fractal Parameters (FPs). Moreover, the bit allocation for each parameter of FPs and the development of the Fractal Coding is discussed at the next Subsections 2.2, 2.3, 2.4 [14]. Fig.…”

Section: Fractal Codingmentioning

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: Fractal Codingmentioning

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.

Self Cite

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“…With the increasing demand in applications of digital image, there is a consistent research in the area of image compression algorithms [13]. The intention is to characterize an original RGB image in the lowest number of bits without dropping the critical content.…”

Section: Image Compression Algorithmsmentioning

confidence: 99%

Evaluation of graphic effects embedded image compression

Jittawiriyanukoon¹,

Srisarkun²

2020

IJECE

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A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.

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“…Regarding the hiding technique, audio steganography is classified into temporal (time) domain [13][14][15], transform domain [11] [20][21][22][23][24][25][26][27] and compressed [28] [29]. Time domains are characterized by high hiding capacity and imperceptibility yet low robustness.…”

Section: Introductionmentioning

confidence: 99%

Ameliorating LSB Using Piecewise Linear Chaotic Map and One-Time Pad for Superlative Capacity, Imperceptibility and Secure Audio Steganography

Elshoush

Mahmoud

2023

IEEE Access

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Audio steganography hides a secret message into an audio. Existing techniques are lacking in achieving high payload, imperceptibility in addition to robustness at the same time. They also suffer from choosing the samples and even LSBs in an unpredictable fashion. Moreover, few adaptive techniques exist, besides not many embed in higher LSBs. Hence, a novel LSB PW LCM method that ameliorates LSB audio steganography is proposed. It uses piecewise linear chaotic map (PWLCM) to embed a secret message in random samples, besides selecting one of the 4-LSBs in an unsystematic way. It is noteworthy that each time a distinct sample and hence a differed 4-LSB is chosen as per different generated PWLCM. At first, Huffman coding is used to lessen the secret message size. Thereafter, to ameliorate the security of the onetime pad, random numbers are generated using PWLCM as an input key. This gives the proposed method a dual protection by amalgamating steganography with enhanced secure one-time pad. MATLAB is used to implement the proposed LSB PW LCM method and evaluate the imperceptibility between cover and stego audios against standard parameters viz. Perceptual Evaluation of Speech Quality (PESQ) and Perceptual Evaluation of Audio Quality (PEAQ). Furthermore, its imperceptibility was tested using Mean Square Error, Peak Signal to Noise Ratio, Signal-to-Noise Ratio, Percentage Root Mean Square Difference and Audio Fidelity. Exhaustive experiments on vastly used metrics affirm that the proposed method LSB PW LCM excel prevailing methods regarding hiding capacity and imperceptibility. Furthermore, it is resistant to brute force attacks having a large key space besides its dependency on the secret message size. In addition, it effectively withstood statistical analysis, specifically histogram attack and fourth first moments. Albeit it was vigorous towards re-sampling attacks, yet it was not very robust against LSB attacks nor noise. Assuredly, it prevails over existing methods and beyond comparison when juxtaposed with them affirming its efficacy.

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Image and audio fractal compression: comprehensive review, enhancements and research directions

Cited by 10 publications

References 22 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

Evaluation of graphic effects embedded image compression

Ameliorating LSB Using Piecewise Linear Chaotic Map and One-Time Pad for Superlative Capacity, Imperceptibility and Secure Audio Steganography

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