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There has been significant interest in developing a scalable version of the High Efficiency Video Coding (HEVC) standard. As expected, the HEVC scalable video version increases the complexity of the codec compared to the nonscalable counterpart. In this paper, we propose an adaptive early-termination interlayer motion prediction mode search that significantly reduces HEVC/SVC's coding complexity by up to 85.77%, while maintaining the overall bitrate.

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During lung sound recordings, heart sounds (HS) interfere with clinical interpretation of lung sounds over the low frequency components which is significant especially at low flow rates. Hence, it is desirable to cancel the effect of HS on lung sound records. In this paper, a novel HS cancellation method is presented. This method first localizes HS segments using multiresolution decomposition of the wavelet transform coefficients, then removes those segments from the original lung sound record and estimates the missing data via a 2D interpolation in the time-frequency (TF) domain. Finally, the signal is reconstructed into the time domain. To evaluate the efficiency of the TF filtering, the average power spectral density (PSD) of the original lung sound segments with and without HS over four frequency bands from 20 to 300 Hz were calculated and compared with the average PSD of the filtered signals. Statistical tests show that there is no significant difference between the average PSD of the HS-free original lung sounds and the TF-filtered signal for all frequency bands at both low and medium flow rates. It was found that the proposed method successfully removes HS from lung sound signals while preserving the original fundamental components of the lung sounds.

Heart beat is an unavoidable source of interference during lung sound recording. This disturbance is more significant at low and medium breathing flow rates. Removing heart sounds (HS) from lung sound recordings or vice versa is a challenging task but of great interest for respiratory specialists and cardiologists. In this study, to separate the two signals, a novel HS separation method based on Independent Component Analysis (ICA) is developed. This method applies an ICA algorithm to the spectrograms of two simultaneous lung sound recordings obtained at two different locations on the chest and yields the independent spectrograms of the separated signals. Then, by implementing the Inverse Short Time Fourier Transform (ISTFT), the separated signals are reconstructed in the time domain. The method was applied to data of two healthy subjects. Analysis of the results as well as subjective inspections indicate the efficiency of the proposed method in terms of HS separation from lung sounds.

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