Intra-cortical signals are usually affected by high levels of noise (0 dB SNR is not uncommon) either due to the recording equipment or to magnetical and electrical couplings between surrounding sources and the recording system. Besides from hindering effective exploitation of the information content in the signals, noise also influences the bandwidth needed to transmit them, which is a problem especially when a large number of channels are to be recorded. In this paper we propose a novel technique for joint denoising and compression of intra-cortical signals based on the Minimum Description Length principle (MDL). This method was tested on simulated signals and the results showed that the proposed technique achieves improvements in SNR (up to .6 dB over MNML for very noisy signals) and compression ratios greater than alternative denoising/compression methods.

We propose a linear predictive coding technique for multichannel electromyographic (EMG) recordings. The signals are acquired using two-dimensional grid of electrodes which generate strongly correlated signals. Previous work only considered spectral redundancy across the signal matrix. In this paper we exploit the correlation present in the residual signals, i.e., the signals after the short term prediction. The proposed technique achieves a compression ratio of about 1divide9, i.e., slightly better than spectral-only decorrelation methods, but with a strong increase of approximately 3.2 dB SNR in the quality of the reconstructed waveform.

In this paper we extend a lossy compression technique for surface EMG signals, which is based on the algebraic code excited linear prediction (ACELP) paradigm, to compress multi-channel surface EMG recordings by exploiting the correlation between the line spectral frequencies (LSF). Experimental results show that the LSFs of the inner signals in a multi-channel recording can be efficiently represented with 13 bit/frame, versus the 38 bit/frame needed by independent ACELP coding of each signal, thus saving 66% of the bandwidth needed to transmit these coefficients while maintaining comparable performance in terms of the SNR, average rectified value and root mean square of the waveform, and mean and median frequencies of the power spectrum.

In this paper we investigate a lossy coding technique for surface EMG signals which is based on the algebraic code excited linear prediction (ACELP) paradigm, widely used for speech signal coding. The algorithm was adapted to the EMG characteristics and tested on both simulated and experimental signals. A fixed compression ratio of 87.3% was chosen. On simulated signals, the mean square error in signal reconstruction and the percentage error in average rectified value after compression were 10.43 % and 5.52 %, respectively. On experimental signals, they were 6.74% and 3.11%. The mean power spectral frequency and third order power spectral moment were estimated with relative error smaller than 1.36% and 1.70%, respectively, for simulated signals, and 3.74% and 2.28% for experimental signals. It was concluded that the proposed coding scheme can be effectively used for high rate, low distortion and low-delay compression of surface EMG signals

Despite the great interest towards long term recordings of electromyographic (EMG) signals, which find applications, for example, in telemedicine, only a few studies have dealt with the compression of these signals. We propose a lossy coding technique for surface EMG signals. The technique is based on the linear predictive coding paradigm widely used for speech compression. The algorithm was tested on both simulated and experimental signals. Mean frequency, median frequency, variance, skewness and kurtosis of the EMG signals were preserved with an error less than 3% with respect to the original values for synthetic signals and experimental signals, reducing the bitrate from 24 kbit/s (12 kbit/s after downsampling) to 352 bit/s, with a compression factor of 97.1%. It was concluded that the linear predictive coding paradigm can be effectively used for high rate compression of surface EMG signals when preservation of only the power spectrum of the signal is of interest. This has applications in ergonomics and occupational medicine.

A common joint source-channel (JSC) decoder structure for predictively encoded sources involves first forming a JSC decoding estimate of the prediction residual and then feeding this estimate to a standard predictive decoding (synthesis) filter. In this paper, we demonstrate that in a JSC decoding context, use of this standard filter is suboptimal. In place of the standard filter, we choose the synthesis filter coefficients to give a least-squares (LS) estimate of the original source, based on given training data. For first-order differential pulse-code modulation, this yields as much as 0.65-dB gain in reconstructing first-order Gauss-Markov sources. More gains are achieved with modest additional complexity by increasing the filter order. While performance can also be enhanced by increasing the source's Markov model order and/or the decoder's lookup table memory, complexity grows exponentially in these parameters. For both predictive and nonpredictive coding, our LS approach offers a strategy for increasing the estimation accuracy of JSC decoders while retaining manageable complexity.

Lossless coding of video sequences is becoming increasingly attractive for applications as diverse as digital cinema, medical imaging and professional video processing. We present a new low-delay, low-complexity algorithm for lossless color video compression. The proposed technique adaptively exploits temporal, spatial and spectral redundancy. Key features of the coder are a backward-adaptive temporal predictor, an intra-frame spatial predictor and adaptive optimal weighting of both predictive components. The residual error is entropy coded by a context-based arithmetic encoder. The proposed lossless video encoder delivers significantly higher compression gains than traditional approaches at approximately the same complexity and delay, enabling efficient storage and communications for emerging lossless video applications.

We present our new low-complexity compression algorithm for lossless coding of video sequences. This new coder produces better compression ratios than lossless compression of individual images by exploiting temporal as well as spatial and spectral redundancy. Key features of the coder are a pixel-neighborhood backward-adaptive temporal predictor, an intra-frame spatial predictor and a differential coding scheme of the spectral components. The residual error is entropy coded by a context-based arithmetic encoder. This new lossless video encoder outperforms state-of-the-art lossless image compression techniques, enabling more efficient video storage and communications.