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首页> 外文会议>Image Quality and System Performance IV; Proceedings of SPIE-The International Society for Optical Engineering; vol.6494; Electronic Imaging Science and Technology >Image Quality and Automatic Color Equalization
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Image Quality and Automatic Color Equalization

机译：图像质量和自动色彩均衡

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In the professional movie field, image quality is mainly judged visually. In fact, experts and technicians judge and determine the quality of the film images during the calibration (post production) process. As a consequence, the quality of a restored movie is also estimated subjectively by experts [26,27].On the other hand, objective quality metrics do not necessarily correlate well with perceived quality [28]. Moreover, some measures assume that there exists a reference in the form of an "original" to compare to, which prevents their use in digital restoration field, where often there is no reference to compare to. That is why subjective evaluation is the most used and most efficient approach up to now.But subjective assessment is expensive, time consuming and does not respond, hence, to the economic requirements of the field [29,25].Thus, reliable automatic methods for visual quality assessment are needed in the field of digital film restoration. Ideally, a quality assessment system would perceive and measure image or video impairments just like a human being. The ACE method, for Automatic Color Equalization [1,2], is an algorithm for digital images unsupervised enhancement. Like our vision system ACE is able to adapt to widely varying lighting conditions, and to extract visual information from the environment efficaciously.We present in this paper is the use of ACE as a basis of a reference free image quality metric. ACE output is an estimate of our visual perception of a scene. The assumption, tested in other papers [3,4], is that ACE enhancing images is in the way our vision system will perceive them, increases their overall perceived quality. The basic idea proposed in this paper, is that ACE output can differ from the input more or less according to the visual quality of the input image In other word, an image appears good if it is near to the visual appearance we (estimate to) have of it. Reversely bad quality images will need "more filtering". Test and results are presented.
机译：在专业电影领域，主要通过视觉来判断图像质量。实际上，专家和技术人员会在校准（后期制作）过程中判断并确定胶片图像的质量。结果，恢复的电影的质量也由专家主观评估[26,27]。另一方面，客观质量指标并不一定与感知质量有很好的关联[28]。而且，一些措施假定存在以“原始”形式进行比较的参考，这妨碍了它们在数字恢复领域的使用，而在数字恢复领域中通常没有可参考的参考。这就是为什么主观评估是迄今为止最常用和最有效的方法的原因。但是，主观评估昂贵，耗时且无法响应该领域的经济要求[29,25]。因此，可靠的自动方法在数字电影恢复领域需要视觉质量评估。理想情况下，质量评估系统将像人类一样感知并测量图像或视频损伤。 ACE方法用于自动色彩均衡[1,2]，是一种用于数字图像无监督增强的算法。像我们的视觉系统一样，ACE能够适应广泛变化的照明条件，并有效地从环境中提取视觉信息。我们在本文中介绍的是使用ACE作为免费参考图像质量指标的基础。 ACE输出是对场景的视觉感知的估计。在其他论文[3,4]中测试的假设是，ACE增强图像会以我们的视觉系统感知它们的方式提高其整体感知质量。本文提出的基本思想是，根据输入图像的视觉质量，ACE输出可能会或多或少地与输入有所不同。换句话说，如果图像接近我们（估计）的视觉外观，则它看起来会很好。有它。相反，质量差的图像将需要“更多过滤”。给出测试和结果。

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