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EDDMF:一个高效的深度差异测量框架,用于全参考光场图像质量评估

Zhengyu Zhang, Shishun Tian, Wenbin Zou

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|November 15, 2023

概括

这项研究引入了一种新的深度学习框架,用于评估光场图像 (LFI) 质量. 该方法有效地测量了参考和扭曲的LFI补丁之间的差异,以进行准确的质量评估.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

对于沉浸式体验的日益增长的需求推动了光场图像 (LFI) 质量评估方面的研究.
现有的方法在有效评估LFI质量下降时可能面临挑战.

研究的目的:

提出一个高效的深度差异测量框架,用于全面参考LFI质量评估.
开发一种新型指标,准确评估扭曲的LFI质量退化.

主要方法:

一个补丁生成模块提取空间角和亚光圈补丁,以减少计算成本.
使用CNN的层次差异网络从空间角度补丁中提取特征.
作为补充信息,使用来自次孔径补丁的局部差异特征.
角度主导和空间主导特征被结合用于补丁质量评估.

主要成果:

与最先进的指标相比,拟议的框架实现了更高的性能.
在LFI质量评估中显示较低的计算复杂性.
在四个代表性的LFI数据集上进行验证.

结论:

开发的框架是使用深度学习的第一个基于补丁的,完全参考的LFI质量评估指标.
为LFI质量评估提供了高效和有效的解决方案.

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