Abstract-We present a Bayesian model that allows to automatically generate fixations/foveations and that can be suitably exploited for compression purposes. The twofold aim of this work is to investigate how the exploitation of high-level perceptual cues provided by human faces occurring in the video can enhance the compression process without reducing the perceived quality of the video and to validate such assumption with an extensive and principled experimental protocol.To such end, the model integrates top-down and bottom-up cues to choose the fixation point on a video frame: at the highest level, a fixation is driven by prior information and by relevant objects, namely human faces, within the scene; at the same time, local saliency together with novel and abrupt visual events contribute by triggering lower level control. The performance of the resulting video compression system has been evaluated with respect to both the perceived quality of foveated video clips and the compression gain with an extensive evaluation campaign, which has eventually involved 200 subjects.Index Terms-Foveated video coding, foveation filtering, image coding, face detection, video quality measurement.

No abstract

In this paper, we propose a characterization of the dynamic behavior of an \ud evolutionary algorithm (EA) with fitness sharing as a function of both the \ud niche radius and the population size. Such a characterization, given in terms of \ud the mean and the standard deviation of the number of niches found during the \ud evolution, can be applied to any EA employing a proportional selection mechanism \ud and does not make any assumption on either the fitness landscape or the internal \ud parameters of the EA itself. On the basis of the proposed characterization, a \ud method for estimating the optimal values for the population size and the niche \ud radius without any a priori information on the fitness landscape is presented and \ud tested on a standard set of functions. The proposed method also provides the best \ud solution for the problem at hand, i.e., the solution obtained in correspondence \ud of such optimal values, at no additional cost

No abstract