A Signal Processing Approach to Modeling Visual Masking

目前处理供人类使用的图像的最先进算法将图像视为信号；这些技术之所以成功，是因为先进的信号处理技术和基于人类视觉系统（HVS）模型的信号处理。然而，许多应用程序最好采用更高层次的图像视图，考虑颜色、结构甚至对象内容。在这个更高层次的视图中，没有图像或HVS处理的统一模型；这样的模型可以大大提高当前图像处理的水平。这项研究包括开发这样一个模型，并将其纳入实际算法，如低速率压缩、允许识别的低速率面部压缩和医学成像。人类视觉系统最初进行低级信号分析，最终以认知结束。信号分析阶段的许多模型预测了对简单刺激的反应，但这些模型对于自然图像来说是失败的，因为人类对结构的高阶处理产生了认知效应，而这些效应不能仅用对简单刺激的反应来建模。认知被认为是在输入加工过程中逐渐发生的；HVS中没有一个单独的部分单独负责识别或将内部表示转换为抽象概念。从信号分析到认知的连续视觉处理表明，基于信号处理的HVS建模可以扩展到包括一些结构处理。本研究通过三个心理物理实验建立了这样的模型。首先是一个受控掩蔽研究，允许开发一个包含结构的视觉掩蔽模型。第二项评价观察者接受扭曲的意愿，将接受程度与第一项研究的检测结果联系起来。第三个比较了使用结构和基于信号的表示表示的图像的识别时间，量化了认知任务中结构的使用。

Current state-of-the-art algorithms that process images for human usetreat images as signals; these techniques are successful because of both advanced signal processing techniques and signal-processing based human visual system (HVS) models. Many applications, however are better approached with a higher-level view of images, considering color, structure, or even object content. No unified model of either images or the HVS processing exists at this higher-level view; such a model can substantially advance the current state-of-the-art in image processing. This research involves developing such a model and incorporating it into practical algorithms such as low-rate compression, low-rate facial compression allowing recognition, and medical imaging.The human visual system initially performs low-level signal analysis and ultimately ends in cognition. Many models of the signal analysis stage which predict responses to simple stimuli, but these models fail for natural images because humans' higher-order processing of structure produces cognitive effects which cannot be modeled using only responses to simple stimuli. Cognition is thought to gradually occur during input processing; no single portion of the HVS is singularly responsible for recognition or transformation of the internal representation to an abstract concept. The continuum of visual processing from signal analysis to cognition suggests that signal-processing-based modeling of the HVS can be extended to include some structural processing. This research develops such models through three psychophysical experiments. The first is a controlled masking study allowing development of a visual masking model incorporating structure. The second rates observers' willingness to accept distortions, relating acceptance with detection results from the first study. The third compares recognition times for images represented using structural and signal-based representations, quantifying the use of structure in cognitive tasks.