Steven Schmidt

Towards the delay sensitivity of games: There is more than genres

Cloud gaming promises many advantages from the user and game developer perspective. But besides its benefits, cloud gaming suffers from two bottlenecks: bandwidth and latency. Although, many researchers have identified delay as an important factor on the QoE in gaming, the cause of varying tolerances towards delay in different games is not yet well understood. In this paper, we want to show that delay sensitivity should not be generalized for an entire game or genre. Instead, a specific scenario with its underlying characteristics should be considered, since differences within the same game can be higher than differences to another game. To investigate the delay sensitivity of games, we assessed quality features such as control, difficulty, delay perception, annoyance, and fairness for highly similar scenarios only differing in their pace or perspective. Results show that changing the pace within the same game can lead to stronger differences in respect to the delay sensitivity than using another game type. Furthermore, we explain how quality features might be used in the judgement process of a player and define a set of input action metrics for a future delay sensitivity based classification of games.

An Evaluation of Video Quality Assessment Metrics for Passive Gaming Video Streaming

Video Quality assessment is imperative to estimate and hence manage the Quality of Experience (QoE) in video streaming applications to the end-user. Recent years have seen a tremendous advancement in the field of objective video quality assessment (VQA) metrics, with the development of models that can predict the quality of the videos streamed over the Internet. However, no work so far has attempted to study the performance of such quality assessment metrics on gaming videos, which are artificial and synthetic and have different streaming requirements than traditionally streamed videos. Towards this end, we present in this paper a study of the performance of objective quality assessment metrics for gaming videos considering passive streaming applications. Objective quality assessment considering eight widely used VQA metrics is performed on a dataset of 24 reference videos and 576 compressed sequences obtained by encoding them at 24 different resolution-bitrate pairs. We present an evaluation of the performance behavior of the VQA metrics. Our results indicate that VMAF predicts subjective video quality ratings the best, while NIQE turns out to be a promising alternative as a no-reference metric in some scenarios.

Towards the need satisfaction in gaming: A comparison of different gaming platforms

Recent advances in Virtual Reality (VR) technologies have resulted in a wider availability of Head Mounted Displays (HMDs). However, it is still unclear if VR gaming offers a substantial added value to players. For this reason a comparison of gaming experiences on VR HMD to those on mobile and PC, two other popular gaming platforms, is performed by conducting a user study via two games available on all three platforms. We explore the QoE of gaming by investigating momentous dimensions using the Player Experience of Need Satisfaction (PENS) questionnaire. The results show higher Presence and Autonomy obtained by using HMD when compared to the two other platforms. However, these factors alone did not improve the Overall Quality. To take advantage of the new technology, satisfaction of all psychological needs, especially Competency, must be assured.

Towards improving visual attention models using influencing factors in a video gaming context

Visual attention models (VAMs) have proved useful in various multimedia applications such as video encoding, interface design, and user behavior prediction. However, existing VAMs are not generalizable enough to be utilized in all multimedia applications implying that in each application there exist some unique factors which drive the user attention. Application agnostic VAMs rely on video and image low level features such as motion, intensity, and orientation while for an interactive application such as gaming, other factors also affect the results. There have been few attempts to identify and measure these factors to date. In this paper, we investigate a variety of the influencing factors in three different categories: user, system, and context.