Improved fractal image compression using range block size

Ismail, B. Mohammed; Basha, Sheikh M.; Reddy, B. Eswara

doi:10.1109/cgvis.2015.7449938

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Finally, a compressed result is encoded using Huffman encoding is improved upon. Mohammed S. Mahaboob Ismail.B Basha et al [33] said improved image fractal image compression (IFIC) method for color images made for the variable block size. The image here is divided into blocks considering the maximum and minimum range.…”

Section: Literature Surveymentioning

confidence: 99%

Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

Naaman

2021

AJRCoS

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Image compression research has increased dramatically as a result of the growing demands for image transmission in computer and mobile environments. It is needed especially for reduced storage and efficient image transmission and used to reduce the bits necessary to represent a picture digitally while preserving its original quality. Fractal encoding is an advanced technique of image compression. It is based on the image's forms as well as the generation of repetitive blocks via mathematical conversions. Because of resources needed to compress large data volumes, enormous programming time is needed, therefore Fractal Image Compression's main disadvantage is a very high encoding time where decoding times are extremely fast. An artificial intelligence technique similar to a neural network is used to reduce the search space and encoding time for images by employing a neural network algorithm known as the “back propagation” neural network algorithm. Initially, the image is divided into fixed-size and domains. For each range block its most matched domain is selected, its range index is produced and best matched domains index is the expert system's input, which reduces matching domain blocks in sets of results. This leads in the training of the neural network. This trained network is now used to compress other images which give encoding a lot less time. During the decoding phase, any random original image, converging after some changes to the Fractal image, reciprocates the transformation parameters. The quality of this FIC is indeed demonstrated by the simulation findings. This paper explores a unique neural network FIC that is capable of increasing neural network speed and image quality simultaneously.

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Section: Literature Surveymentioning

confidence: 99%

Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

Naaman

2021

AJRCoS

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“…while 80 percent of patients heal without any care. [31] Elderly men, children, men who already have cardiovascular disease, obesity , and diabetes are vulnerable to COVID-19. [30] COVID-19's clinical symptoms are respiratory disorder, fatigue, dry cough, tiredness, etc.…”

Section: Introductionmentioning

confidence: 99%

Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms

Prakash¹

2020

IJETER

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COVID-19, Corona Virus Diasease-2019, belongs to genus of Coronaviridae. A virus with no vaccine creating unpredictable havocs in the human lives and financial and economic systems in every country throughout the world. It is precariously halted everything in the society mercilessly. An analysis on COVID-19 datasets to understand which age group is mostly effected due to COVID-19. Different prediction models are built using machine learning algorithms and their performances are computed and evaluated. Random Forest Regressor and Random Forest Classifier outperformed the other machine learning models like SVM, KNN+NCA, Decision Tree Classifier, Gaussian Naïve Bayesian Classifier, Multilinear Regression, Logistic Regression and XGBoost Classifier.

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“…Various compression algorithms are used to reduce the size of the images without reducing the quality of images [19]. Algorithms like embedded zero wavelet algorithm, convergent algorithm, Fuzzy vector quantization algorithm etc.…”

Section: Fractal Compressionmentioning

confidence: 99%

Fractal Compression Technique for Color Images Using Variable Block

Ahmed¹,

Sattar²

2017

IJIVP

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The main intention of Fractal Image Compression is to reduce the size of image and maintain good level of their reconstructed image. A major issue in Fractal Image Compression is decrease in image quality, compression ratio and PSNR. To overcome these issues we employ Fractal transformation with entropy coding. There are two phases in the proposed approach. In the first phase color images are separated into three RGB planes using variable range block size. In second phase by applying the inverse transform and iterative functions the image is restored. It is observed that the results are improving in fractal compression for both gray images as well as color images. In this work high CR and PSNR is observed compared to fixed block range and other existing methods. The proposed work yields better CR of 20 and high PSNR.

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Improved fractal image compression using range block size

Cited by 9 publications

References 19 publications

Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms

Fractal Compression Technique for Color Images Using Variable Block

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