In the last decade, Autism Spectrum Disorder (ASD) prevalence rate has significantly increased, which consequently led to the expansion of research and expenditure in the field, predominantly focusing on searching for the cause. In a typical classroom scenario, working with children with ASD very often requires 1:1 teacher to child ratio, which makes it very expensive and difficult to implement. Serious games have been utilised as a medium for teaching various developmental skills, such as social interaction, speech, motor skills development, emotion recognition, and other basic concepts. Designing serious games for ASD population differs from other games and even other serious games significantly. It requires a holistic approach with extensive knowledge and expertise from fields other than computer science, such as psychology, sociology and cognitive science. However, once harnessed correctly, such games can be used by children with ASD on their own time, with or without supervision and they can be educational. In addition, they can adjust the appropriate pace while at the same time providing feedback in form of reinforcement and correction. Applying the rules of science of learning and teaching, one can design games that are educational for all types of learners, including children with ASD. In this paper, two independent user studies have been conducted, demonstrating how serious gaming and e-learning principles can be harnessed in order to intervene, develop or strengthen pivotal developmental skills, like learning novel vocabulary, counting, identifying numbers and colours, and responding to inference questions. We have tested the educational e-book with children diagnosed with ASD and with typically developing children to assess skill acquisition in native language for children with ASD and in English, a foreign language, for typically developing children to demonstrate the educational aspect of the game for all types of learners. We showed that the same e-book in two languages can be used for teaching different types of learners through a fun and engaging medium.

Interactive digital storytelling has become a popular method for virtual cultural heritage presentations. Combinations of stories and 3D virtual reconstructions are attractive for the audience and have high edutainment values. In this paper we investigate if 360◦ VR videos further contribute to user immersion in the preservation of intangible cultural heritage. It describes a case study of the Mostar bridge diving project, aimed to present and preserve the bridge diving tradition from the Old Bridge in Mostar, Bosnia and Herzegovina. It is a virtual reality application which enables the user to virtually jump off the bridge after watching 360◦ video stories about its history and the bridge diving tradition and upon successfully completing the quiz evaluation of the knowledge gained from the stories. The user experience evaluation study shows that our method was successful in preserving a form of intangible heritage and posits suggestions that can be used in developing an intangible heritage preservation framework.

Image compression standards rely on predictive coding, transform coding, quantization and entropy coding, in order to achieve high compression performance. Very recently, deep generative models have been used to optimize or replace some of these operations, with very promising results. However, so far no systematic and independent study of the coding performance of these algorithms has been carried out. In this paper, for the first time, we conduct a subjective evaluation of two recent deep-learning-based image compression algorithms, comparing them to JPEG 2000 and to the recent BPG image codec based on HEVC Intra. We found that compression approaches based on deep auto-encoders can achieve coding performance higher than JPEG 2000, and sometimes as good as BPG. We also show experimentally that the PSNR metric is to be avoided when evaluating the visual quality of deep-learning-based methods, as their artifacts have different characteristics from those of DCT or wavelet-based codecs. In particular, images compressed at low bitrate appear more natural than JPEG 2000 coded pictures, according to a no-reference naturalness measure. Our study indicates that deep generative models are likely to bring huge innovation into the video coding arena in the coming years.

The demand for very high-resolution video content in entertainment services (4K, 8K, panoramic, 360 VR) puts an increasing load on the distribution network. In order to reduce the network usage in existing delivery infrastructure for such services while keeping a good quality of experience, dynamic spatial video adaptation at the client side is seen as a key feature, and is actively investigated by academics and industrials. However, the impact of spatial adaptation on quality perception is not clear. In this paper, we propose a methodology for the evaluation of such adapted content, conduct a series of perceived quality measurements and discuss results showing potential benefits and drawbacks of the technique. Based on our results, we also propose a signaling mechanism in MPEG-DASH to assist the client in its spatial adaptation logic.

Subjective quality assessment is considered a reliable method for quality assessment of distorted stimuli for several multimedia applications. The experimental methods can be broadly categorized into those that rate and rank stimuli. Although ranking directly provides an order of stimuli rather than a continuous measure of quality, the experimental data can be converted using scaling methods into an interval scale, similar to that provided by rating methods. In this paper, we compare the results collected in a rating (mean opinion scores) experiment to the scaled results of a pairwise comparison experiment, the most common ranking method. We find a strong linear relationship between results of both methods, which, however, differs between content. To improve the relationship and unify the scale, we extend the experiment to include cross-content comparisons. We find that the cross-content comparisons reduce the confidence intervals for pairwise comparison results, but also improve the relationship with mean opinion scores.

The dynamic range of real world scenes may vary from around 102 to greater than 107 , whilst the dynamic range of monitors may vary from 102 to 105 . In this paper, we investigate the impact of the dynamic range ratio (DRratio) between the captured scene and the displayed image, upon the value of system gamma preferred by subjects (a simple global power law transformation applied to the image). To do so, we present an image dataset with a broad distribution of dynamic ranges upon various subranges of a SIM2 monitor. The full dynamic range of the monitor is 105 and we present images using either the full range, 75% or 50% of this, while maintaining a fixed mid-luminance level. We find that the preferred system gamma is inversely correlated with the DRratio and importantly, is one (linear) when the DRratio is one. This strongly suggests that the visual system is optimized for processing images only when the dynamic range is presented correctly. The DRratio is not the only factor. By using 50% of the monitor dynamic range and using either the lower, middle or upper portion of the monitor, we show that increasing the overall luminance level also increases the preferred system gamma, although to a lesser extent than the DR ratio.

Computing dynamic range of high dynamic range (HDR) content is an important procedure when selecting the test material , designing and validating algorithms, or analyzing aesthetic attributes of HDR content. It can be computed on a pixel-based level, measured through subjective tests or predicted using a mathematical model. However, all these methods have certain limitations. This paper investigates whether dynamic range of modeled images with no semantic information, but with the same first order statistics as the original, natural content, is perceived the same as for the corresponding natural images. If so, it would be possible to improve the perceived dynamic range (PDR) pre-dictor model by using additional objective metrics, more suitable for such synthetic content. Within the subjective study, three experiments were conducted with 43 participants. The results show significant correlation between the mean opinion scores for the two image groups. Nevertheless, natural images still seem to provide better cues for evaluation of PDR.

High dynamic range (HDR) imaging has become an important topic in both academic and industrial domains. Nevertheless, the concept of dynamic range (DR), which underpins HDR, and the way it is measured are still not clearly understood. The current approach to measure DR results in a poor correlation with perceptual scores (r ≈ 0.6). In this paper, we analyze the limitations of the existing DR measure, and propose several options to predict more accurately subjective DR judgments. Compared to the traditional DR estimates, the proposed measures show significant improvements in Spearman's and Pearson's correlations with subjective data (up to r ≈ 0.9). Despite their straightforward nature, these improvements are particularly evident in specific cases, where the scores obtained by using the classical measure have the highest error compared to the perceptual mean opinion score.