Symmetry-Based Biomedical Image Compression

Bairagi, Vinayak K.

doi:10.1007/s10278-015-9779-3

Cited by 16 publications

(11 citation statements)

References 9 publications

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“…combination of ridgelet transformation with hybrid neural network produce better result as compare to JPEG2K .Since outcome produce grayscale images, the color component is not given much value and ridgelet might be ineffective where edges are curved rather than being straight line. In 2014, M. Moorthi et al [24] elaborated integration model which uses separation of ROI and non ROI region where ROI is compressed using Curvelet transform , DPCM and non ROI is compressed using IWT , SPIHT followed by adaptive arithmetic encoding. After fusion results obtained .Performance is degraded in IWT when edges are smooth curve.…”

Section: Radiological Image Compression Methodologies (Ricm)mentioning

confidence: 99%

Radiological Image Compression (RIC) Methodologies for Telemedicine

2019

IJRTE

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The goal of this review paper is to systematically analyze various Radiological Image Compression (RIC) mechanisms and conclude the optimal technique among them. A brief introduction of image compression reversible and irreversible methodologies are presented and summarized. To improve the classical lossless compression of low efficiency, a way of image lossless compression with high efficiency is offered. The work followed Efficient XML Interchange (EXI) binary xml technology and progressed consequences will be achieved. Moreover enhanced solution for RIC using EXI is likewise mentioned

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Section: Radiological Image Compression Methodologies (Ricm)mentioning

confidence: 99%

Radiological Image Compression (RIC) Methodologies for Telemedicine

2019

IJRTE

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“…We directly discard such nuisances from the data to improve compression. Others have used symmetries in X for lossless compression of multisets [21], graphs [56][57][58], or structured images [59][60][61][62][63]. We, instead, use invariance of the tasks Y for lossless prediction.…”

Section: Related Workmentioning

confidence: 99%

Lossy Compression for Lossless Prediction

Dubois¹,

Bloem-Reddy²,

Ullrich³

et al. 2021

Preprint

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Most data is automatically collected and only ever "seen" by algorithms. Yet, data compressors preserve perceptual fidelity rather than just the information needed by algorithms performing downstream tasks. In this paper, we characterize the bit-rate required to ensure high performance on all predictive tasks that are invariant under a set of transformations, such as data augmentations. Based on our theory, we design unsupervised objectives for training neural compressors. Using these objectives, we train a generic image compressor that achieves substantial rate savings (more than 1000× on ImageNet) compared to JPEG on 8 datasets, without decreasing downstream classification performance.Preprint. Under review.

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“…Contextual vector quantization is employed in [7], and 3D wavelet coders are used in [29]. A symmetry-based approach is discussed in [1,28]. Although these methods have been proven to be efficient for medical image compression, it is not possible to run them on a CPU fast enough to provide 25 fps for on-the-fly decompression, particularly in the case of medium or low-end devices.…”

Section: Image 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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Symmetry-Based Biomedical Image Compression

Cited by 16 publications

References 9 publications

Radiological Image Compression (RIC) Methodologies for Telemedicine

Radiological Image Compression (RIC) Methodologies for Telemedicine

Lossy Compression for Lossless Prediction

Compression of Image Sequences in Interactive Medical Teleconsultations

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