Frame-based multiple-description video coding with extended orthogonal filter banks

We propose a frame-based multiple-description video coder. The analysis filter bank is the extension of an orthogonal filter bank which computes the spatial polyphase components of the original video frames. The output of the filter bank is a set of video sequences which can be compressed with a standard coder. The filter bank design is carried out by taking into account two important requirements for video coding, namely, the fact that the dual synthesis filter bank is FIR, and that loss recovery does not enhance the quantization error. We give explicit results about the required properties of the redundant channel filter and the reconstruction error bounds in case of packet errors. We show that the proposed scheme has good error robustness to losses and good performance, both in terms of objective and visual quality, when compared to single description and other multiple description video coders based on spatial subsampling. PSNR gains of 5 dB or more are typical for packet loss probability as low as 5%

Image and Collateral Text in Support of Auto-annotation and Sentiment Analysis

We present a brief overview of the way in which image analysis, coupled with associated collateral text, is being used for auto-annotation and sentiment analysis. In particular, we describe our approach to auto-annotation using the graph- theoretic dominant set clustering algorithm and the annotation of images with sentiment scores from SentiWordNet. Preliminary results are given for both, and our planned work aims to explore synergies between the two approaches

Performance Evaluation of Distributed Video Coding Schemes

4nonenoneBernardini, R; Rinaldo, R; Zontone, P; Vitali, ABernardini, Riccardo; Rinaldo, Roberto; Zontone, Pamela; Vitali, A

Dual channel Electrodermal activity sensor for motion artifact removal in car drivers' stress detection

In this paper we present a dual channel sensor for electrodermal activity measurement, with particular attention to the drivers' stress detection. The sensor captures the elec-trodermal signals that are present on the hands of the driver, transmits them via WiFi to a laptop and then the data are processed. In particular, we developed a novel algorithm for the removal of motion artifacts that arise when the driver moves the hands on the steering wheel. We performed several kinds of tests: first in laboratory, then on a professional driving simulator and finally in a real car in city traffic. The algorithm has been compared to several well known algorithms for signal separation. We identified, as an indicator of performances, the spectral flatness of the outputs. In this application, the proposed method outperformed the benchmark algorithms