CIF: Medium: Assessment and Modeling of Temporal Variation in Perceived Audio and Video Quality Using Direct Brainwave Measurement

该项目的目标是更好地模拟人类对音频、视频和视听信号质量变化的感知。然而，实现这一目标的一个根本挑战是开发一种人类主观测试方法，该方法可以在时间上以足够的准确性评估这种感知的质量变化。在这项研究中，不同于简单地让受试者对给定时刻的信号质量进行评级的人体试验，这项研究使用高分辨率脑电图仪(EEG)捕捉每个受试者对不同质量多媒体信号的大脑反应的电模式。通过分析这些EEG信号，可以在人类观察者/听者甚至意识到质量已经改变之前检测到信号的感知质量的变化。然而，主要的困难是在这些试验期间捕获的EEG波形包含大量噪声，因此必须筛选大量数据以识别所收集的EEG波形中对应于感知质量变化的分量。为了完成这一任务，将既使用确定性的时-空-频分析技术，又使用基于信息谱的随机技术。为了建立基于计算机的感知质量模型，将考虑AR/ARMA(自回归/自回归移动平均)建模技术，并设计支持向量机和相关的基于核的分类器来输出对应于感知质量的类别指数。除了其改进视听传输系统的潜力外，这项研究的更广泛的影响包括它可能为从计算机向人类传输信息开辟新的、更有效的途径。

The goal of this project is to better model the human perception of quality variations in audio, video, and audiovisual signals. A fundamental challenge in accomplishing this goal, however, is to develop a human subjective testing methodology that can assess such the perceived quality variations with sufficient accuracy in time. Rather than conducting human trials that simply ask subjects to rate the signal quality at given moments in time, in this research the electrical patterns of each subject's brain responses to multimedia signals of varying quality are captured using a high resolution electroencephalograph (EEG). By analyzing these EEG signals, it becomes possible to detect a change in the perceived quality of the signal before the human observer/listener even becomes consciously aware that the quality has changed.A major difficulty, however, is that the EEG waveforms captured during these trials contain large amounts of noise, and it is therefore necessary to sift through a large set of data to identify the components of the collected EEG waveforms that correspond to changes in perceived quality. To accomplish this task, both deterministic time-space-frequency analysis techniques will be applied as well as stochastic techniques based on information spectra. To create computer-based models of perceived quality, AR/ARMA (autoregressive/autoregressive moving average) modeling techniques will be considered and support vector machines along with related kernel-based classifiers will be designed to output class indexes corresponding to perceived quality. Beyond its potential for improving audiovisual transmission systems, the broader impacts of this research include the possibility that it will open up new and more efficient avenues for transferring information from computers to human beings.