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FNTF:First No-reference Then Full-reference image quality assessment using Dark Channel

机译：FNTF：先进行无参考，然后使用暗通道进行全参考图像质量评估

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摘要

It is an indispensable step to faithfully evaluate and control the perceptual quality of digital visual applications such as image compression, image restoration and multimedia streaming. In this paper, we propose an efficient and effective twostep framework named First No-reference Then Full-reference (FNTF), to evaluate different kinds of noise, and distinguish the quality of distorted image, using features Natural Scene Statistics of Dark Channel(NSSDC), Average Distance of Matched Keypoints(ADMK) and Dark Channel Similarity Deviation(DCSD) we proposed. Dark Channel is a kind of natural statistics based on the key observation - most local patches in images contain some pixels whose intensity are close to zero in at least one color channel. Features extracted from Dark Channel can greatly represent the pollution level of the image and the kind of noise it sufferd from. The average distance and dark channel similarity are sensitive to image distortions, while different local structures in a distorted image suffer different distance and degrees of similarity, respectively. This motivated us to explore global variation based local quality for overall image quality prediction. We find that our two-step framework can predict accurately perceptual image quality.
机译：忠实评估和控制数字视觉应用程序（例如图像压缩，图像恢复和多媒体流）的感知质量是必不可少的步骤。在本文中，我们提出了一个有效且有效的两步框架，即“先无参考然后全参考”（FNTF），以利用暗通道自然场景统计（NSSDC）功能评估各种噪声并区分失真图像的质量。 ），我们提出的匹配关键点的平均距离（ADMK）和暗通道相似性偏差（DCSD）。暗通道是一种基于关键观察的自然统计数据-图像中的大多数局部色块都包含一些像素，这些像素在至少一个颜色通道中的强度接近于零。从暗通道提取的特征可以极大地代表图像的污染水平及其所遭受的噪声。平均距离和暗通道相似度对图像失真敏感，而失真图像中的不同局部结构分别遭受不同的距离和相似度。这促使我们探索基于全局变化的局部质量，以进行整体图像质量预测。我们发现，我们的两步框架可以准确地预测感知图像质量。

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