Context-based lossless and near-lossless compression of EEG signals

Memon, Nasir; Kong, Xuan; Cinkler, J.

doi:10.1109/4233.788586

Cited by 66 publications

(41 citation statements)

References 14 publications

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“…Single-channel EEG compression is widely studied, and can be categorized under lossless, near-lossless, and lossy methods (see [8] and references therein). Predictive-based coders are competitive in lossless [9] and near-lossless [10,11] scenarios, but they do not support progressive transmission and hence they are of little use in practical scenarios. Combining progressive transmission and guaranteed maximum distortion (in L ∞ sense) will be crucial in real-time transmission and clinical settings.…”

Section: Introductionmentioning

confidence: 99%

Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions

Dauwels

Srinivasan

Reddy

et al. 2013

IEEE J. Biomed. Health Inform.

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Abstract-A novel near-lossless compression algorithm for multi-channel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. Multi-channel EEG is represented in suitable multi-way (multi dimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multi-way forms of MC-EEG. A compression algorithm is built based on the principle of "lossy plus residual coding," consisting of a matrix/tensor decomposition based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly five-fold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

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Section: Introductionmentioning

confidence: 99%

Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions

Dauwels

Srinivasan

Reddy

et al. 2013

IEEE J. Biomed. Health Inform.

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“…The lossless compression method proposed in [17] tracks the nonstationarities of the different regions of the image and then the LS based predictor is updated based on this information. There are a few compression methods such as [16][17][18][19][20][21][22], which are developed for different types of images. But all these methods (except [16] and [22]) are computationally much more expensive than CALIC.…”

Section: Introductionmentioning

confidence: 99%

A minimum entropy based switched adaptive predictor for lossless compression of images

Tiwari

Kumar

2008

SIViP

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The gradient adjusted predictor (GAP) uses seven fixed slope quantization bins and a predictor is associated with each bin, for prediction of pixels. The slope bin boundary in the same appears to be fixed without employing a criterion function. This paper presents a technique for slope classification that results in slope bins which are optimum for a given set of images. It also presents two techniques that find predictors which are statistically optimal for each of the slope bins. Slope classification and the predictors associated with the slope bins are obtained off-line. To find a representative predictor for a bin, a set of least-squares (LS) based predictors are obtained for all the pixels belonging to that bin. A predictor, from the set of predictors, that results in the minimum prediction error energy is chosen to represent the bin. Alternatively, the predictor is chosen, from the same set, based on minimum entropy as the criterion. Simulation results, of the proposed method have shown a significant improvement in the compression performance as compared to the GAP. Computational complexity of the proposed method , excluding the training process, is of the same order as that of GAP.Keywords GAP · MED · Gradient · LS-based predictor ·

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“…Besides, adaptive prediction is achieved in most of the coders by using multi-predictor structures [13]- [23]. Among which, the context-based adaptive lossless image coding (CALIC) system [16], a state-of-the-art lossless coder proposed for JPEG-LS, uses a gradient adjusted predictor (GAP).…”

Section: Introductionmentioning

confidence: 99%

Least-Squares-Based Switching Structure for Lossless Image Coding

Kau

Lin

2007

IEEE Trans. Circuits Syst. I

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Abstract-Many coding methods are more efficient with some images than others. In particular, run-length coding is very useful for coding areas of little changes. Adaptive predictive coding achieves high coding efficiency for fast changing areas like edges. In this paper, we propose a switching coding scheme that will combine the advantages of both run-length and adaptive linear predictive coding. For pixels in slowly varying areas, run-length coding is used; otherwise least-squares (LS)-adaptive predictive coding is used. Instead of performing LS adaptation in a pixel-by-pixel manner, we adapt the predictor coefficients only when an edge is detected so that the computational complexity can be significantly reduced. For this, we use a simple yet effective edge detector using only causal pixels. This way, the proposed system can look ahead to determine if the coding pixel is around an edge and initiate the LS adaptation in advance to prevent the occurrence of a large prediction error. With the proposed switching structure, very good prediction results can be obtained in both slowly varying areas and pixels around boundaries. Furthermore, only causal pixels are used for estimating the coding pixels in the proposed encoder; no additional side information needs to be transmitted. Extensive experiments as well as comparisons to existing state-of-the-art predictors and coders will be given to demonstrate its usefulness.

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Context-based lossless and near-lossless compression of EEG signals

Cited by 66 publications

References 14 publications

Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions

Near-Lossless Multichannel EEG Compression Based on Matrix and Tensor Decompositions

A minimum entropy based switched adaptive predictor for lossless compression of images

Least-Squares-Based Switching Structure for Lossless Image Coding

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