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Assessing Texture Dimensions and Video Quality in Motion Pictures using Sensory Evaluation Techniques

机译:使用感觉评估技术评估运动图像中的纹理尺寸和视频质量

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The quality of images and videos is usually examined with well established subjective tests or instrumental models. These often target content transmitted over the internet, such as streaming or videoconferences and address the human preferential experience. In the area of high-quality motion pictures, however, other factors are relevant. These mostly are not error-related but aimed at the creative image design, which has gained comparatively little attention in image and video quality research. To determine the perceptual dimensions underlying movie-type video quality, we combine sensory evaluation techniques extensively used in food assessment – Degree of Difference test and Free Choice Profiling – with more classical video quality tests. The main goal of this research is to analyze the suitability of sensory evaluation methods for high-quality video assessment. To understand which features in motion pictures are recognizable and critical to quality, we address the example of image texture properties, measuring human perception and preferences with a panel of image-quality experts. To this aim, different capture settings were simulated applying sharpening filters as well as digital and analog noise to exemplary source sequences. The evaluation, involving Multidimensional Scaling, Generalized Procrustes Analysis as well as Internal and External Preference Mapping, identified two separate perceptual dimensions. We conclude that Free Choice Profiling connected with a quality test offers the highest level of insight relative to the needed effort. The combination enables a quantitative quality measurement including an analysis of the underlying perceptual reasons.
机译:通常用完善的主观测试或乐器模型检查图像和视频的质量。这些经常通过互联网传输的目标内容,例如流或视频会议,并解决人类优先经验。然而,在高质量的运动图片领域,其他因素是相关的。这些主要是与错误相关但旨在创意图像设计,这在图像和视频质量研究中获得了相对较少的关注。为了确定电影型视频质量的潜在尺寸,我们将广泛用于食品评估的感官评估技术相结合 - 差异测试程度和自由选择分析 - 具有更古典的视频质量测试。本研究的主要目的是分析高质量视频评估的感官评估方法的适用性。要了解运动图片中的哪些功能是可识别的,并且对质量至关重要,我们解决了图像纹理属性的示例,用一块图像质量专家测量人类感知和偏好。为此目的,模拟不同的捕获设置将锐化过滤器以及数字和模拟噪声应用于示例性源序列。评估涉及多维缩放,广义促进分析以及内部和外部偏好映射,确定了两个单独的感知尺寸。我们得出结论,与优质测试相关的自由选择分析提供了相对于所需努力的最高洞察力。该组合使定量质量测量能够分析潜在的感知原因。

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