Ricerc@Sapienza

Facial microexpressions (MEs) play a crucial role in the non verbal communication. Their automatic detection and recognition on a real video is a topic of great interest in different fields. However, the main difficulty in automatically capturing this kind of feature consists in its rapid temporal evolution, i.e. MEs occur in very few frames of video acquired by a conventional camera. In this paper a first study concerning the perceptual characteristics of ME is performed.

This paper aims at increasing the visual quality of a blurred image according to the contrast sensitivity of a human observer. The main idea is to enhance those image details which can be perceived by a human observer without introducing annoying visible artifacts. To this aim, an adaptive wavelet decomposition is applied to the original blurry image. This decomposition splits the frequency axis into subbands whose central frequency and amplitude width are built according to the contrast sensitivity.

This paper focuses on an entropy based formalism to speed up the evaluation of the Structural SIMilarity(SSIM) index in images affected by a global distortion. Looking at images as information sources, avisualdistortion typical setcan be defined for SSIM. This typical set consists of just a subset of information belongingto the original image and the corresponding one in the distorted version. As side effect, some general theoreticalcriteria for the computation of any full reference quality assessment measure can be given in order to maximizeits computational efficiency.