Abstract:
A time series of face images of a human during a human activity are captured. A first artificial neural network (ANN) processing pipeline processes the captured time series of face images to provide a first attention level indicator signal. An electrophysiological signal indicative of the level of attention of the human during the activity is also captured. A second ANN processing pipeline processes the sensed electrophysiological signal to providing a second attention level indicator signal. A risk indicator signal is then generated based on at least one of the first attention level indicator and second attention level indicator. A user circuit is then triggered as a result of the risk indicator reaching or failing to reach at least one attention level threshold.
Abstract:
In an embodiment, a first individual image and a second individual image constituting an encoded stereoscopic image, for example in JPEG format with respective levels of encoding quality and united in a multiple-image file, for example of the Multiple-Picture Object (MPO) type. The second level of encoding quality is lower than the first level of encoding quality. During decoding, the first individual image encoded with a first level of encoding quality and the second individual image encoded with a second level of encoding quality lower than the first level of encoding quality are extracted from the multiple-image file, then using information of the first extracted individual image for enhancing the second extracted individual image.