K. R. Rao

Lossless multi-channel EEG compression

This paper presents a method for losslessly compressing multi-channel electroencephalogram signals. The Karhunen-Loeve transform is used to exploit the inter-correlation among the EEG channels. The transform is approximated using lifting scheme which results in a reversible realization under finite precision processing. An integer time-frequency transform is applied to further minimize the temporal redundancy

Lossless Audio Coding Using the IntMDCT and Rounding Error Shaping

In this paper, lossless audio coding using the integer modified discrete cosine transform (IntMDCT) is discussed. The IntMDCT is constructed as an integer approximation of the MDCT using the lifting scheme and is reversible. The rounding error shape of the IntMDCT is derived. When the spectral energy of the input audio signal is concentrated at the low frequencies, the rounding error spectrum limits the lossless coding performance. A method for shaping the rounding error in the transform domain is presented. This rounding error shaping scheme manipulates the error so that it is below the spectral envelope of the signal at the high frequencies in order to improve the lossless coding performance for the signal. Examples of an error shaping filter design are presented and verified by simulations. An IntMDCT-based lossless coding implementation is carried out to illustrate the use of the error shaping filters

Improved lossless audio coding using the noise-shaped IntMDCT

This paper discusses approximation noise shaping to improve the efficiency of the integer modified discrete cosine transform (IntMDCT)-based lossless audio codec. The scheme is applied to rounding operations associated with lifting steps to shape the noise spectrum towards the low frequency bands. In this paper, constraints on the noise shaping filter and a design procedure with the constraints are discussed. Several noise shaping filters are designed and experimental results showing the improvement are presented.

Multichannel SVD-based image de-noising

In this paper, we propose a multichannel SVD-based image de-noising algorithm. The IntDCT is employed to decorrelate the image into sixteen subbands. The SVD is then applied to each of the subbands and the additive noise is reduced by truncating the eigenvalues. The simulation results illustrate that this technique can effectively filter the noisy images without assuming any statistics of the image by using a data compression technique.

Reduced Complexity Space-Time-Frequency Model for Multi-Channel EEG and Its Applications

Searching for an efficient summarization of multi-channel electroencephalogram (EEG) behavior is a challenging signal analysis problem. Recently, parallel factor analysis (PARAFAC) is reported as an efficient tool for extracting features of multi-channel EEG by simultaneously employing space-time-frequency knowledge, i.e. decomposing multi-channel EEG signal into a linear combination of its space-time-frequency feature. However, this decomposition scheme suffers from expensive computational load when applied to either long term or high number of channels EEG signals. In this paper, a reduced computational complexity space-time-frequency model for multi-channel EEG signal is proposed by dividing selected content into segments yielding additional segment signatures. By carefully selecting the number of segments, features extracted from the proposed model are comparable with those from the conventional space-time-frequency model while the time used in computation is reduced by more than 50%. Simulation results show that the proposed model can efficiently extract eye blink artifact from background EEG. Furthermore, classification accuracy when employing the proposed model to brain computer interface (BCI) application is also comparable with the conventional model.

A modified hybrid DCT-SVD image-coding system for color image

This paper proposes a hybrid image-coding system that combines the DCT and the SVD. A criterion based on the standard deviation of 8/spl times/8 blocks of an image is used to choose between DCT and SVD. The DCT is used to transform the high correlated blocks of the image, while the SVD is used to transform the low correlated blocks. The AMVQ is used to encode the eigenvectors of the blocks encoded using the SVD and the uniform quantization is used to encode the DCT coefficients. The simulation results show good image quality at low bit rates.

A comparison of integer fast Fourier transforms for lossless coding

The lifting scheme-based integer fast Fourier transform (IntFFT), an integer approximation of the FFT, is reversible. When it is used for lossless coding applications, the computational complexity and approximation error increase due to realization of the trivial butterflies by three lifting steps. Since the error appears as a noise floor and it limits the lossless coding efficiency, it is desirable to reduce not only the computational complexity but also the noise floor level as much as possible. This survey presents two schemes to realize an improved IntFFT in terms of the number of arithmetic operations and the level of the noise floor. The first scheme is based on employment of two/three lifting step schemes with combined rounding operations, and the second one is the multidimensional lifting (MDL) scheme. The improvement is shown by comparing the number of arithmetic operations and rounding operations to compute the IntFFT and also by comparing levels of the noise floor. In addition, an improvement in lossless coding efficiency due to the reduced noise floor can be predicted by observing the reduced estimated entropy of the IntFFT coefficients.

Approximation error analysis for transform-based lossless audio coding

The integer modified discrete cosine transform (IntMDCT), an integer approximation of the MDCT, is a reversible transform realized by the lifting scheme and thus is a useful transform for lossless audio coding. Because of the integer approximation, however, the approximation error appears as a noise floor in the transform domain and limits the lossless coding efficiency. In this paper, a theoretical analysis of the approximation error of the IntMDCT is discussed. The result is then used to design a simple test filter applied to each rounding operation of the IntMDCT in such a way that the error spectrum is shaped towards the low frequencies. As a result, especially when the spectral energy of an input signal is concentrated in the low frequency domain, the lossless coding efficiency is improved.