Selection of compression test images using variance-based statistical method

Allaoui, Chems El Houda; Bassou, Abdesselam; Benyahia, Ismahane; Khelifi, Mustapha

doi:10.11591/ijeecs.v16.i1.pp243-258

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…The compression process's objective determine the evaluation metric to be utilized. Furthermore, other parameters, such as output image correctness and noise content, are considered depending on the topic and research setting [60], [61]. Many evaluation metrics can be applied to measure the efficiency of the performance of a compression algorithm, such as the CR, MSE, PNSR, time and SSIM.…”

Section: A Performance Metricsmentioning

confidence: 99%

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

Aziz,

Mohd Sam,

Hassan

et al. 2023

IEEE Access

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In this modern era, medical image sharing has become a routine activity within hospital information systems. Digital medical images have become valuable resources that aid health care systems' decision-making and treatment procedures. A medical image consumes a significant amount of memory, and the size of medical images continues to grow as medical imaging technology progresses. In addition, an image is shared for analysis to support knowledge sharing and disease diagnosis. Therefore, health care systems must ensure that medical images are appropriately distributed without information loss in a timely and secure manner. Image compression is the primary process performed on each medical image before it is shared to ensure that the purpose of sharing an image is accomplished. The hybrid region of interest-based medical compression algorithms reduces image size. Furthermore, these algorithms shorten the image compression process time by manipulating the advantages of lossy and lossless compression techniques. A comprehensive review of previous studies that utilized this approach was conducted. Sample studies were selected from published articles in an open database subscribed to by Universiti Teknologi Malaysia for ten years (2012 to 2023). This work aims to critically review and comprehensively analyze previous types of algorithms by focusing on their main performance results: compression ratio, mean square error and peak signal-to-noise ratio. This article will identify which type of algorithm can give optimal value to the primary performance metric for compressing medical images. INDEX TERMS DICOM, Hybrid Image Compression, Medical Image, Performance Review, Region of Interest-based, I. INTRODUCTIONMedical images are critical resources that assist physicians and facilitate monitoring of a patient's health condition, providing a wide range of information that doctors and specialists need to make accurate diagnoses. Moreover, medical images are among the most critical assets of a health care information system (HIS). Health care practitioners utilize these images to electronically detect, diagnose, treat and evaluate various diseases.In the current technological era, image files occupy a significant amount of memory. The same issue applies to medical images. Each medical image consumes a vast amount of memory and storage space. Therefore, an HIS administrator must ensure sufficient memory space for storing medical images. In addition, the records of many patients must be retained for an extended period, consuming more space for storing images [1], [2].The primary operation that involves medical images in an HIS is medical image sharing. With the challenges that we are currently facing due to the Coronavirus disease 2019 (COVID-19) pandemic, the need for image sharing has become particularly evident as movement is restricted in many places worldwide. In many clinical cases, diagnostic planning requires collaboration between referring physicians

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Section: A Performance Metricsmentioning

confidence: 99%

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

Aziz,

Mohd Sam,

Hassan

et al. 2023

IEEE Access

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The Discrete Quincunx Wavelet Packet Transform

Bassou¹

2021

Wavelet Theory

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This chapter aims to present an efficient compression algorithm based on quincunx wavelet packet transform that can be applied on any image of size 128 × 128 or bigger. Therefore, a division process into sub-images of size 128 × 128 was applied on three gray-scale image databases, then pass each sub-image through the wavelet transform and a bit-level encoder, to finally compress the sub-image with respect to a fixed bit rate. The quality of the reconstructed image is evaluated using several parameters at a given bit rate. In order to improve the quality in sense of the evaluation quality, an exhaustive search has led to the best packet decomposition base. Two versions of the proposed compression scheme were performed; the optimal version is able to decrease the effect of block boundary artifacts (caused by the image division process) by 27.70 % considering a natural image. This optimal version of the compression scheme was compared with JPEG standard using the quality evaluation parameters and visual observation. As a result, the proposed compression scheme presents a competitive performance to JPEG standard; where the proposed scheme performs a peak signal to noise ratio of 0.88 dB over JPEG standard at a bit rate of 0.50 bpp for a satellite image.

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Selection of compression test images using variance-based statistical method

Cited by 2 publications

References 8 publications

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

A Performance Review for Hybrid Region of Interest-Based Medical Image Compression

The Discrete Quincunx Wavelet Packet Transform

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