Near-lossless compression of medical images through entropy-coded DPCM

Chen, K.; Ramabadran, Tenkasi V.

doi:10.1109/42.310885

Cited by 154 publications

(48 citation statements)

References 7 publications

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“…Intensive applications can be found in telephony, digital systems, video signals, or image processing [1], [25] and in the medical domain; for instance, magnetic resonance or ultrasound images can be coded using DPCM [2]. This is largely because DPCM is based on the fact that most signals, such as images and speech or video signals, show significant correlation between successive samples.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Behavior in a DPCM System with an Order 2 Predictor. Case of a Constant Input

Fournier-Prunaret

2005

Circuits Syst Signal Process

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This paper deals with a differential pulse code modulation (DPCM) transmission system. It includes an encoder which uses a coupling between a transversal predictor and a nonlinear quantizer. This paper studies, from a dynamical point of view, an order 2 DPCM system modeled via a noninvertible two-dimensional map. Chaotic phenomena can be observed; depending on the kind of application, it can be interesting to avoid chaos or to use it. In both cases, it is necessary to understand the mechanisms which give rise to it. For that, the usual tools of chaotic dynamics are used (bifurcation structures, critical and invariant manifolds).

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Section: Introductionmentioning

confidence: 99%

Dynamical Behavior in a DPCM System with an Order 2 Predictor. Case of a Constant Input

Fournier-Prunaret

2005

Circuits Syst Signal Process

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“…It is clear that PSNR and Δ are inversely proportional to each other for all channels. (Chen and Ramabadran 1994) suggest the minimum PSNR of 40-50 db in their research, hence, quantization steps in Figure 4 should be less than 30.…”

Section: Figure 4 Effect Of Quantization Steps On Psnr In Different mentioning

confidence: 90%

Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

Pham

Tran

2017

AJIS

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In the biomedical and healthcare fields, the ownership protection of the outsourced data is becoming a challenging issue in sharing the data between data owners and data mining experts to extract hidden knowledge and patterns. Watermarking has been proved as a rightprotection mechanism that provides detectable evidence for the legal ownership of a shared dataset, without compromising its usability under a wide range of data mining for digital data in different formats such as audio, video, image, relational database, text and software. Time series biomedical data such as Electroencephalography (EEG) or Electrocardiography (ECG) is valuable and costly in healthcare, which need to have owner protection when sharing or transmission in data mining application. However, this issue related to kind of data has only been investigated in little previous research as its characteristics and requirements. This paper proposes an optimized watermarking scheme to protect ownership for biomedical and healthcare systems in data mining. To achieve the highest possible robustness without losing watermark transparency, Particle Swarm Optimization (PSO) technique is used to optimize quantization steps to find a suitable one. Experimental results on EEG data show that the proposed scheme provides good imperceptibility and more robust against various signal processing techniques and common attacks such as noise addition, low-pass filtering, and resampling.

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“…Allowing a certain distortion in the reconstructed imageÎ leads to a higher compression performance. Near-lossless compression was introduced for applications that require a tight bound in the l ∞ norm, like remote sensing [1] or medical imaging [2], where a large error in a pixel could potentially induce a wrong classification or diagnosis. Near-lossless techniques bound the l ∞ norm -also known as peak absolute error (PAE) or Maximum Absolute…”

Section: Introductionmentioning

confidence: 99%

A Fully Embedded Two-Stage Coder for Hyperspectral Near-Lossless Compression

Beerten

Blanes

Serra-Sagristà

2015

IEEE Geosci. Remote Sensing Lett.

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Abstract-This letter proposes a near-lossless coder for hyperspectral images. The coding technique is fully embedded and minimizes the distortion in the l2 norm initially and in the l∞ norm subsequently. Based on a two-stage near-lossless compression scheme, it includes a lossy and a near-lossless layer. The novelties are: the observation of the convergence of the entropy of the residuals in the original domain and in the spectral-spatial transformed domain; and an embedded near-lossless layer. These contributions enable a progressive transmission while optimising both SNR and PAE performance. The embeddedness is accomplished by bitplane encoding plus arithmetic encoding. Experimental results suggest that the proposed method yields a highly competitive coding performance for hyperspectral images, outperforming multi-component JPEG2000 for l∞ norm and pairing its performance for l2 norm, and also outperforming M-CALIC in the near-lossless case -for PAE ≥ 5-.

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Near-lossless compression of medical images through entropy-coded DPCM

Cited by 154 publications

References 7 publications

Dynamical Behavior in a DPCM System with an Order 2 Predictor. Case of a Constant Input

Dynamical Behavior in a DPCM System with an Order 2 Predictor. Case of a Constant Input

Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining

A Fully Embedded Two-Stage Coder for Hyperspectral Near-Lossless Compression

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