Efficient calculation of sensor utility and sensor removal in wireless sensor networks for adaptive signal estimation and beamforming

Wireless sensor networks are often deployed over a large area of interest and therefore the quality of the sensor signals may vary significantly across the different sensors. In this case, it is useful to have a measure for the importance or the so-called "utility" of each sensor, e.g., for sensor subset selection, resource allocation or topology selection. In this paper, we consider the efficient calculation of sensor utility measures for four different signal estimation or beamforming algorithms in an adaptive context. We use the definition of sensor utility as the increase in cost (e.g., mean-squared error) when the sensor is removed from the estimation procedure. Since each possible sensor removal corresponds to a new estimation problem (involving less sensors), calculating the sensor utilities would require a continuous updating of different signal estimators (where is the number of sensors), increasing computational complexity and memory usage by a factor. However, we derive formulas to efficiently calculate all sensor utilities with hardly any increase in memory usage and computational complexity compared to the signal estimation algorithm already in place. When applied in adaptive signal estimation algorithms, this allows for on-line tracking of all the sensor utilities at almost no additional cost. Furthermore, we derive efficient formulas for sensor removal, i.e., for updating the signal estimator coefficients when a sensor is removed, e.g., due to a failure in the wireless link or when its utility is too low. We provide a complexity evaluation of the derived formulas, and demonstrate the significant reduction in computational complexity compared to straightforward implementations

JND-Based Perceptual Video Coding for 4:4:4 Screen Content Data in HEVC

The JCT-VC standardized Screen Content Coding (SCC) extension in the HEVC HM RExt + SCM reference codec offers an impressive coding efficiency performance when compared with HM RExt alone; however, it is not significantly perceptually optimized. For instance, it does not include advanced HVS-based perceptual coding methods, such as JND-based spatiotemporal masking schemes. In this paper, we propose a novel JND-based perceptual video coding technique for HM RExt + SCM. The proposed method is designed to further improve the compression performance of HM RExt + SCM when applied to YCbCr 4:4:4 SC video data. In the proposed technique, luminance masking and chrominance masking are exploited to perceptually adjust the Quantization Step Size (QStep) at the Coding Block (CB) level. Compared with HM RExt 16.10 + SCM 8.0, the proposed method considerably reduces bitrates (Kbps), with a maximum reduction of 48.3%. In addition to this, the subjective evaluations reveal that SC-PAQ achieves visually lossless coding at very low bitrates.Comment: Preprint: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

A new model-based algorithm for optimizing the MPEG-AAC in MS-stereo

International audienceIn this paper, a new model-based algorithm for optimizing the MPEG-Advanced Audio Coder (AAC) in MS-stereo mode is presented. This algorithm is an extension to stereo signals of prior work on a statistical model of quantization noise. Traditionally, MS-stereo coding approaches replace the Left (L) and Right (R) channels by the Middle (M) and Sides (S) channels, each channel being independently processed, almost like a monophonic signal. In contrast, our method proposes a global approach for coding both channels in the same process. A model for the quantization error allows us to tune the quantizers on channels M and S with respect to a distortion constraint on the reconstructed channels L and R as they will appear in the decoder. This approach leads to a more efficient perceptual noise-shaping and avoids using complex psychoacoustic models built on the M and S channels. Furthermore, it provides a straightforward scheme to choose between LR and MS modes in each subband for each frame. Subjective listening tests prove that the coding efficiency at a medium bitrate (96 kbits/s for both channels) is significantly better with our algorithm than with the standard algorithm, without increase of complexity