Data Analysis in Multimedia Quality Assessment: Revisiting the Statistical Tests

Assessment of multimedia quality relies heavily on subjective assessment, and is typically done by human subjects in the form of preferences or continuous ratings. Such data is crucial for analysis of different multimedia processing algorithms as well as validation of objective (computational) methods for the said purpose. To that end, statistical testing provides a theoretical framework towards drawing meaningful inferences, and making well grounded conclusions and recommendations. While parametric tests (such as t test, ANOVA, and error estimates like confidence intervals) are popular and widely used in the community, there appears to be a certain degree of confusion in the application of such tests. Specifically, the assumption of normality and homogeneity of variance is often not well understood. Therefore, the main goal of this paper is to revisit them from a theoretical perspective and in the process provide useful insights into their practical implications. Experimental results on both simulated and real data are presented to support the arguments made. A software implementing the said recommendations is also made publicly available, in order to achieve the goal of reproducible research

On the perceptual similarity of realistic looking tone mapped High Dynamic Range images

International audienceHigh Dynamic Range (HDR) images are usually displayed on conventional Low Dynamic Range (LDR) displays because of the limited availability of HDR displays. For the conversion of the large dynamic luminance range into the eight bit quantized values, parameterized Tone Mapping Operators (TMO) are applied. Human observers are able to optimize the parameters in order to get the highest Quality of Experience by judging the displayed LDR images on a realism scale. In the study presented in this paper, two TMOs with three parameters each were evaluated by observers in a subjective experiment. Although the chosen parameter settings vary largely, the chosen images appear to have the same QoE for the observers. In order to assess this similarity objectively, three commonly used image quality measurement algorithms were applied. Their agreement with the preference of the observers was analyzed and it was found that the Visual Difference Predictor (VDP) outperforms the Structural Similarity Index and the Root Mean Square Error. A threshold value for VDP is derived that indicates when two LDR images appear to have the same Quality of Experience

How is Gaze Influenced by Image Transformations? Dataset and Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial network (dubbed GazeGAN). A modified UNet is proposed as the generator of the GazeGAN, which combines classic skip connections with a novel center-surround connection (CSC), in order to leverage multi level features. We also propose a histogram loss based on Alternative Chi Square Distance (ACS HistLoss) to refine the saliency map in terms of luminance distribution. Extensive experiments and comparisons over 3 datasets indicate that GazeGAN achieves the best performance in terms of popular saliency evaluation metrics, and is more robust to various perturbations. Our code and data are available at: https://github.com/CZHQuality/Sal-CFS-GAN

Amour, désir et maladie dans la littérature romantique

Dans la littérature romantique, amour et maladie vont couramment de pair, et bon nombre d\u27héroïnes succombent à une maladie impitoyable, la phtisie, souvent associée à l\u27amour pur et noble (alors que les maladies vénériennes caractérisent des relations douteuses). Ce constat est le point de départ de mon étude, qui, sans négliger le poids des réalités sanitaires, propose une autre explication, tenant à la représentation romantique de l\u27amour. Fondée sur l\u27idéalisation des êtres et la négation des corps, elle  rend problématique l\u27expression du désir et la concrétisation charnelle. La maladie serait donc un moyen d\u27exprimer physiquement cette contradiction et la souffrance qu\u27elle suscite. Par ailleurs, la mort (par quelque maladie que ce soit) est un moyen de clôturer tragiquement la relation amoureuse en pleine akmè, et d\u27éviter ainsi le problème de la durée, du mariage et du prosaïsme. Il s\u27agirait donc d\u27un choix esthétique.