International audienceIn this chapter different factors that may influence Quality of Experience (QoE) in the context of media consumption, networked services, and other electronic communication services and applications, are discussed. QoE can be subject to a range of complex and strongly interrelated factors, falling into three categories: human, system and context influence factors (IFs). With respect to Human IFs, we discuss variant and stable factors that may potentially bear an influence on QoE, either for low-level (bottom-up) or higher-level (top-down) cognitive processing. System IFs are classified into four distinct categories, namely content-, media-, network- and device-related IFs. Finally, the broad category of possible Context IFs is decomposed into factors linked to the physical, temporal, social, economic, task and technical information context. The overview given here illustrates the complexity of QoE and the broad range of aspects that potentially have a major influence on it

Almost all mesh processing procedures cause some more or less visible changes in the appearance of objects represented by polygonal meshes. In many cases, such as mesh watermarking, simplification or lossy compression, the objective is to make the change in appearance negligible, or as small as possible, given some other constraints. Measuring the amount of distortion requires taking into account the final purpose of the data. In many applications, the final consumer of the data is a human observer, and therefore the perceptibility of the introduced appearance change by a human observer should be the criterion that is taken into account when designing and configuring the processing algorithms. In this review, we discuss the existing comparison metrics for static and dynamic (animated) triangle meshes. We describe the concepts used in perception-oriented metrics used for 2D image comparison, and we show how these concepts are employed in existing 3D mesh metrics. We describe the character of subjective data used for evaluation of mesh metrics and provide comparison results identifying the advantages and drawbacks of each method. Finally, we also discuss employing the perception-correlated metrics in perception-oriented mesh processing algorithms.

International audienceStereoscopic imaging is becoming very popular and its deployment by means of photography, television, cinema. . .is rapidly increasing. Obviously, the access to this type of images imposes the use of compression and transmission that may generate artifacts of different natures. Consequently, it is important to have appropriate tools to measure the quality of stereoscopic content. Several studies tried to extend well-known metrics, such as the PSNR or SSIM, to 3D. However, the results are not as good as for 2D images and it becomes important to have metrics dealing with 3D perception. In this work, we propose a full reference metric for quality assessment of stereoscopic images based on the binocular fusion process characterizing the 3D human perception. The main idea consists of the development of a model allowing to reproduce the binocular signal generated by simple and complex cells, and to estimate the associated binocular energy. The difference of binocular energy has shown a high correlation with the human judgement for different impairments and is used to build the Binocular Energy Quality Metric (BEQM). Extensive experiments demonstrated the performance of the BEQM with regards to literature