A Signal Processing Approach to Modeling Visual Masking

视觉掩蔽建模的信号处理方法

• 批准号: 0514311
• 负责人: Sheila Hemami
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0514311&HistoricalAwards=false
• 关键词: Signal; Processing; Approach; Modeling; Visual

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.

目前的国家的最先进的算法，处理图像为人类使用处理图像作为信号，这些技术是成功的，因为这两个先进的信号处理技术和基于信号处理的人类视觉系统（HVS）模型。 然而，许多应用程序更好地处理图像的高级视图，考虑颜色，结构甚至对象内容。 在这个更高级别的视图中，不存在图像或HVS处理的统一模型;这样的模型可以大大推进图像处理中的当前最先进技术。 该研究涉及开发这样一个模型，并将其纳入实际算法，如低速率压缩，低速率面部压缩允许识别和医学成像。人类视觉系统最初执行低级别信号分析，最终以认知为终点。 信号分析阶段的许多模型预测对简单刺激的反应，但这些模型对自然图像失败，因为人类对结构的高阶处理产生的认知效应不能仅使用对简单刺激的反应来建模。 认知被认为是在输入处理过程中逐渐发生的;没有一个HVS的单一部分单独负责识别或将内部表征转换为抽象概念。从信号分析到认知的视觉处理连续体表明，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发展了这样的模型。 第一个是一个受控的掩蔽研究，允许开发一个视觉掩蔽模型，将结构。 第二个评价观察者接受失真的意愿，将接受程度与第一个研究的检测结果联系起来。 第三个比较识别时间的图像表示使用结构和基于信号的表示，量化的认知任务中的结构的使用。