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，该方法可以及时以足够的准确度评估这种感知质量变化。 在这项研究中，每个受试者的大脑对不同质量的多媒体信号的反应的电模式是使用高分辨率脑电图仪（EEG）捕获的，而不是进行简单地要求受试者在给定时刻对信号质量进行评级的人体试验。 通过分析这些EEG信号，可以在人类观察者/收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改变。然而，主要困难在于，在这些试验期间捕获的EEG波形包含大量噪声，并且因此有必要筛选大的数据集以识别所收集的EEG波形中对应于感知质量变化的分量。 为了完成这一任务，确定性的时间-空间-频率分析技术将被应用，以及基于信息谱的随机技术。 为了建立以计算机为基础的感知质量模型，将考虑AR/阿尔马（自回归/自回归移动平均）建模技术，并设计支持向量机沿着相关的基于核的分类器，以输出与感知质量相对应的类别指数。 除了改进视听传输系统的潜力之外，这项研究的更广泛影响包括它可能为从计算机向人类传输信息开辟新的和更有效的途径。

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.