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Reduced-reference quality assessment for retargeted images

机译:重新定位图像的参考质量降低评估

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Recent years have witnessed tremendous growth in the generation and consumption of digital images. Monitoring and evaluating image quality is an important issue for online and mobile media applications. Conventional quality assessment work mostly focus on intensity level distortion caused by operations that do not change image size. In this work, we study the problem of quality assessment for images having undergone content-adaptive resizing, also known as retargeting operations. We design a reduced-reference algorithm to analyze the structural distortion caused by retargeting and propose a quality score that achieves positive correlation with human observations. The quality score and distortion analysis from the proposed algorithm provide rich information for both objective and subjective quality assessment tasks.
机译:近年来,数字图像的产生和消费已见证了巨大的增长。监视和评估图像质量是在线和移动媒体应用程序的重要问题。传统的质量评估工作主要集中在不改变图像尺寸的操作引起的强度水平失真上。在这项工作中,我们研究了已经进行了内容自适应调整大小(也称为重新定位操作)的图像的质量评估问题。我们设计了一种减少参考的算法来分析由重定目标引起的结构变形,并提出与人类观察结果呈正相关的质量得分。所提出算法的质量得分和失真分析为客观和主观质量评估任务提供了丰富的信息。

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