Lossless Image Compression using Adaptive Predictive Coding of Selected Seed Values

Al-Khafaji, Ghadah; Al-Mahmood, Haider

doi:10.5120/ijca2016909605

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…In prediction, an approximation of the image to the original one is created (Al-Khafaji, 2012). The predicted image relies on the least difference values, either from the vertical or horizontal value or the left bottom value of the current pixel (Al-Khafaji and Al-Mahmood, 2016). In this section, some commonly used predictive techniques for lossless image compression and MED predictor are discussed.…”

Section: Related Workmentioning

confidence: 99%

Improved Median Edge Detection (iMED) for Lossless Image Compression

et al. 2023

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A wide variety of applications are used in lossless image compression models, especially in medical, space, and aerial imaging domains. Predictive coding improves the performance of lossless image compression, which highly relies on entropy error. Lower entropy error results in better image compression. The main focus of this research is to improve the prediction process by minimizing the entropy error. This paper proposes a novel idea for improved Median Edge Detection (iMED) predictor for lossless image compression. MED predictor is improved using k-means clustering and finding the local context of pixels using 20-Dimensional Difference (DDx20) for input images and updates the cluster weights using learning rates (µi) to minimize the prediction errors of pixels. The performance of the proposed predictor is evaluated on the standard grey-scale test images dataset and KODAK image dataset. Results are obtained and compared based on entropy error, bits per pixel (bpp), and computational running time in seconds(s) with the MED, GAP, FLIF, and LBP predictors. The performance of the proposed iMED predictor improves significantly in terms of the entropy error, bpp, and computational running time in seconds(s) after comparison with different state-of-the-art predictors.

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Section: Related Workmentioning

confidence: 99%

Improved Median Edge Detection (iMED) for Lossless Image Compression

et al. 2023

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“…by entropy encoders(Al-Khafaji & Al-Mahmood, 2016). Prediction and entropy encoding are two major steps of predictive coding for lossless compression of an image (Shridevi&Vijaykumar,2012)asshowninFigure2.Inpredictivecodingtechnique,everypixelofanimageispredictedindividuallyfromthecontext inrasterscanformat.Afterprediction,residualimageisobtainedbytakingdifferenceoforiginal image and predicted image as shown in Figure2.…”

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confidence: 99%

A Block-Based Arithmetic Entropy Encoding Scheme for Medical Images

Sharma

Sood

Puthooran

et al. 2020

International Journal of Healthcare Information Systems and Informatics

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The digitization of human body, especially for treatment of diseases can generate a large volume of data. This generated medical data has a large resolution and bit depth. In the field of medical diagnosis, lossless compression techniques are widely adopted for the efficient archiving and transmission of medical images. This article presents an efficient coding solution based on a predictive coding technique. The proposed technique consists of Resolution Independent Gradient Edge Predictor16 (RIGED16) and Block Based Arithmetic Encoding (BAAE). The objective of this technique is to find universal threshold values for prediction and provide an optimum block size for encoding. The validity of the proposed technique is tested on some real images as well as standard images. The simulation results of the proposed technique are compared with some well-known and existing compression techniques. It is revealed that proposed technique gives a higher coding efficiency rate compared to other techniques.

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Correlated Hierarchical Autoregressive Models Image Compression

Al-Khafaji¹,

Hasan²,

Noaman³

2017

djps

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In this paper, a Correlated Hierarchical Autoregressive Model (CHARM) method for image compression is proposed. It based on using multi-layered modeling concept of correlated autoregressive coefficients, which is a modified version of Hierarchical Autoregressive Model (HARM). The test results indicate that the suggested techniques improve the compression ratio along with preserving the image quality compared to traditional predictive coding or autoregressive model and HARM on a series of selected images.

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Lossless Image Compression using Adaptive Predictive Coding of Selected Seed Values

Abstract: In this paper, two proposed effective selection seed values techniques adopts to enhance the predictive coding efficiency.

Cited by 4 publications

References 9 publications

Improved Median Edge Detection (iMED) for Lossless Image Compression

Improved Median Edge Detection (iMED) for Lossless Image Compression

A Block-Based Arithmetic Entropy Encoding Scheme for Medical Images

Correlated Hierarchical Autoregressive Models Image Compression

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