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Full-reference image quality metric for blurry images and compressed images using hybrid dictionary learning

机译：使用混合字典学习的模糊图像和压缩图像的全引用图像质量指标

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摘要

The image quality degradation due to the loss of high-frequency components of images is often seen in real scenarios, such as artifacts caused by image compression and image blur caused by camera shake or out of focus. Quantifying such degradation is very useful for many tasks that are related to image quality. In this paper, an effective approach is proposed for the image quality assessment on images with blur as well as images with compression artifacts. Based on the relation between the dictionaries of the degraded image and the reference image, we build up a hybrid dictionary learning model to characterize the space of patches of the reference image as well as that of the degraded image. The image quality is then measured by the difference between the two resulting dictionaries. Combined with a simple sparse-coding-based metric, the proposed method shows competitive performance on five benchmark datasets, which demonstrates its effectiveness.
机译：由于图像压缩和由摄像机抖动或焦点出来引起的图像压缩和图像模糊引起的伪像而导致图像质量劣化是由于图像的高频分量丢失而导致的图像质量劣化。 量化这种劣化对于与图像质量相关的许多任务非常有用。 本文提出了一种有效的方法，用于模糊图像的图像质量评估以及具有压缩伪影的图像。 基于降级图像和参考图像的词典之间的关系，建立混合字典学习模型，以表征参考图像的斑块的空间以及降级图像。 然后通过两个结果词典之间的差异来测量图像质量。 结合简单的基于稀疏编码度量，该方法在五个基准数据集中显示了竞争性能，这表明其有效性。

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