Massively parallel lossless compression of medical images using least-squares prediction and arithmetic coding

Weinlich, Andreas; Rehm, Johannes; Amon, Peter; Hutter, Andreas; Kaup, André

doi:10.1109/icip.2013.6738346

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…GPUs are intended for extremely parallel arithmetic computations, so it is quite practical to utilize their computational power for better encoding of images. (56) proposed an image encoding system for Nvidia's GPU programming language CUDA that consisted of a pixel-wise prediction stage followed by an arithmetic entropy encoding stage for the prediction error. They offered an extraordinary parallel lossless medical image encoder that could achieve lower data rates comparative to other image and video encoders.…”

Section: Compression Of Medical Datamentioning

confidence: 99%

Evolution and Advancement of Arithmetic Coding over Four Decades

Razaq

et al. 2020

Opn J Sci & Tech

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Arithmetic Coding (AC) is a form of entropy encoding used in lossless data compression. It is a well-known, state of the art technique, in which the frequently seen symbols are encoded with fewer bits than rarely seen symbols. It has been widely used since last four decades. Many researchers worked on it to improve its performance and they successfully experimented with it. This technique has also been in use in combination with other techniques to gain surprising results. In this survey paper, an effort is made to recap a number of accomplishments from 1976 to 2017 regarding Arithmetic Coding. This study provides an insight for new researchers to know how this technique evolved with time and how major achievements were made using this technique. This paper gives a comparison of AC with another well-known technique named Huffman Coding. Comparison with its contemporary counterparts shows that it is better in performance almost in every situation.

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Section: Compression Of Medical Datamentioning

confidence: 99%

Evolution and Advancement of Arithmetic Coding over Four Decades

Razaq

et al. 2020

Opn J Sci & Tech

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“…Multiple parallel algorithms targeted for medical images are available as well. These employ (among others) Discrete Wavelet Transform (DWT) [30], least-squares prediction [31], or Medical Image Lossless Compression (MILC) [27]. A common factor for all of these methods is that they require dedicated hardware that is compatible with CUDA [24] or OpenCL [15] frameworks.…”

Section: Image Compressionmentioning

confidence: 99%

“…Therefore, our method allows for an overall average reduction of data size by 38% for CT data (9,98 bpp instead of 16) and 44% for MR data (9,06 bpp instead of 16). For comparison, state-of-the-art methods based on the CUDA framework [31] report achieving an average of 5,80 bpp for 10 CT datasets. Is it worth noting that a telemedical system may implement multiple algorithms and use them with regard to the available hardware.…”

Section: Lossless Compressionmentioning

confidence: 99%

Compression of Image Sequences in Interactive Medical Teleconsultations

Malawski¹,

Czekierda²

2017

csci

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Interactive medical teleconsultations are an important tool in modern medical practice. Their applications include remote diagnostics, conferences, workshops, and classes for students. In many cases, standard medium or low-end machines are employed, and the teleconsultation systems must be able to provide a high quality of user experience with very limited resources. Particularly problematic are large datasets consisting of image sequences that need to be accessed fluently. The main issue is insufficient internal memory; therefore, proper compression methods are crucial. However, a scenario where image sequences are kept in a compressed format in the internal memory and decompressed onthe-fly when displayed is difficult to implement due to performance issues. In this paper, we present methods for both lossy and lossless compression of medical image sequences that only require compatibility with the Pixel Shader 2.0 standard, which is present even on relatively old, low-end devices. Based on the evaluation of quality, size reduction, and performance, these methods have been proven to be suitable and beneficial for medical teleconsultation applications.

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“…DICOM images are one of the examples of medical captured digital images [9]. Similarly, the process of medical diagnosis produces a huge amount of medical images such as those from computed tomography, magnetic resonance imaging (MRI), and electrocardiogram [20]. As these images take more space and bandwidth, their compression [11] is important for transmission.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

Sreenivasulu

Varadarajan

2018

Journal of Intelligent Systems

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Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.

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Massively parallel lossless compression of medical images using least-squares prediction and arithmetic coding

Cited by 8 publications

References 20 publications

Evolution and Advancement of Arithmetic Coding over Four Decades

Evolution and Advancement of Arithmetic Coding over Four Decades

Compression of Image Sequences in Interactive Medical Teleconsultations

An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

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