CIF:Small:Visual Information Measures for Task-Based Imaging Applications

CIF：小：基于任务的成像应用的视觉信息测量

• 批准号: 0916471
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916471&HistoricalAwards=false
• 关键词: CIF; Small; Visual; Information; Measures

Many digital images are not acquired for documentary evidence or for archival use, but instead are intermediate pieces of information to be ultimately used by a human to assess a situation, make a decision, or reach a conclusion. Often, the image need not provide "picture-perfect quality" in order for a human to successfully perform a task - consider security screening of carry-on baggage, for example. Many tasks can be easily performed with images that would be considered to be severely degraded in a purely aesthetic sense. To date, however, image assessment algorithms have been primarily focused on evaluating aesthetic quality. These algorithms are typically designed for relatively high-quality images, and they do not perform well on highly degraded images which exhibit low aesthetic quality but remain useful for human-performed tasks.This research characterizes the suitability of an image for recognition and the perceived utility in terms of conveying information about content. The characterization is performed in terms of properties of the image, rather than in terms of the response of higher-level vision to the image, and is based upon extensive subjective experiments quantifing the perceived utility of highly degraded images and identifing recognition thresholds for images - maximally degraded images that still allow recognition. A utility measure is developed which takes as input the original image and the distorted image and outputs a distance quantifying the amount of degradation in the image relative to both the recognition threshold (at the low end) and to the original or a visually lossless representation of the original (at the high end). Use of this measure is demonstrated by integration this measure into imaging applications including compression and enhancement.

许多数字图像不是为了文件证据或档案用途而获得的，而是最终被人类用来评估情况、做出决定或得出结论的中间信息片段。通常，为了让人类成功地执行任务，图像不需要提供“完美的画质”--例如，考虑对随身行李进行安全检查。许多任务可以很容易地与图像一起执行，这些图像在纯美学意义上会被认为严重退化。然而，迄今为止，图像评估算法主要集中在评估美学质量上。这些算法通常是为相对高质量的图像设计的，它们不能很好地处理高度退化的图像，这些图像表现出较低的美学质量，但仍然适用于人工执行的任务。本研究表征了图像是否适合识别以及在传达内容信息方面的感知效用。表征是根据图像的属性而不是高级视觉对图像的响应来执行的，并且基于大量的主观实验来量化高度退化图像的感知效用并识别图像的识别阈值-仍然允许识别的最大退化图像。开发了一种实用测量，其将原始图像和失真图像作为输入，并输出量化图像中相对于识别阈值(在低端)和相对于原始图像或原始图像的视觉无损表示(在高端)的降级量的距离。通过将该测量集成到包括压缩和增强在内的成像应用中，展示了该测量的使用。