arXiv subjects

Luca Surace

Publications and source records attributed to Luca Surace.

3 recordsLinked to original sources

arXiv2025

Temporal Brightness Management for Immersive Content

Modern virtual reality headsets demand significant computational resources to render high-resolution content in real-time. Therefore, prioritizing power efficiency becomes crucial, particularly for portable versions reliant on batteries. A significant portion of the energy consumed by these systems is attributed to their displays. Dimming the screen can save a considerable amount of energy; however, it may also result in a loss of visible details and contrast in the displayed content. While contrast may be partially restored by applying post-processing contrast enhancement steps, our work is orthogonal to these approaches, and focuses on optimal temporal modulation of screen brightness. We propose a technique that modulates brightness over time while minimizing the potential loss of visible details and avoiding noticeable temporal instability. Given a predetermined power budget and a video sequence, we achieve this by measuring contrast loss through band decomposition of the luminance image and optimizing the brightness level of each frame offline to ensure uniform temporal contrast loss. We evaluate our method through a series of subjective experiments and an ablation study, on a variety of content. We showcase its power-saving capabilities in practice using a built-in hardware proxy. Finally, we present an online version of our approach which further emphasizes the potential for low level vision models to be leveraged in power saving settings to preserve content quality.

eess.IV↗

arXiv2021

Learning GAN-based Foveated Reconstruction to Recover Perceptually Important Image Features

A foveated image can be entirely reconstructed from a sparse set of samples distributed according to the retinal sensitivity of the human visual system, which rapidly decreases with increasing eccentricity. The use of Generative Adversarial Networks has recently been shown to be a promising solution for such a task, as they can successfully hallucinate missing image information. As in the case of other supervised learning approaches, the definition of the loss function and the training strategy heavily influence the quality of the output. In this work,we consider the problem of efficiently guiding the training of foveated reconstruction techniques such that they are more aware of the capabilities and limitations of the human visual system, and thus can reconstruct visually important image features. Our primary goal is to make the training procedure less sensitive to distortions that humans cannot detect and focus on penalizing perceptually important artifacts. Given the nature of GAN-based solutions, we focus on the sensitivity of human vision to hallucination in case of input samples with different densities. We propose psychophysical experiments, a dataset, and a procedure for training foveated image reconstruction. The proposed strategy renders the generator network flexible by penalizing only perceptually important deviations in the output. As a result, the method emphasized the recovery of perceptually important image features. We evaluated our strategy and compared it with alternative solutions by using a newly trained objective metric, a recent foveated video quality metric, and user experiments. Our evaluations revealed significant improvements in the perceived image reconstruction quality compared with the standard GAN-based training approach.

cs.GR↗

arXiv2017

Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers

Group emotion recognition in the wild is a challenging problem, due to the unstructured environments in which everyday life pictures are taken. Some of the obstacles for an effective classification are occlusions, variable lighting conditions, and image quality. In this work we present a solution based on a novel combination of deep neural networks and Bayesian classifiers. The neural network works on a bottom-up approach, analyzing emotions expressed by isolated faces. The Bayesian classifier estimates a global emotion integrating top-down features obtained through a scene descriptor. In order to validate the system we tested the framework on the dataset released for the Emotion Recognition in the Wild Challenge 2017. Our method achieved an accuracy of 64.68% on the test set, significantly outperforming the 53.62% competition baseline.

cs.CV↗