Full Reference Stereoscopic Objective Quality Assessment using Lightweight Machine Learning

Narúsci S. Bastos, Lucas Seidy Ribeiro Dos Santos Ikenoue, D. Palomino, G. Corrêa, Tatiana Tavares, B. Zatt
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Abstract

Decades of research on Image Quality Assessment (IQA) have promoted the creation of a variety of objective quality metrics that strongly correlate to subjective image quality. However, challenges remain when considering quality assessment of 3D/stereo images. Multiple objective quality metrics for 3D images were designed by extending the well-known 2D metrics. As a result, these solutions tend to present weaknesses under 3D-specific artifacts. Recent works demonstrate the effectiveness of machine-learning techniques in the design of 3D quality metrics. Although effective, some machine learning-based solutions may lead to high computational effort and restrict its adoption in low-latency lightweight systems/applications. This paper presents a study on full-reference stereoscopic objective quality assessment considering lightweight machine learning. We evaluated four different decision tree-based algorithms considering eight distinct sets of image features. The classifiers were trained using data from the Waterloo IVC 3D Image Quality Database to determine the subjective quality score measured using Mean Opinion Score (MOS). The results show that RandomForest generally obtains the best accuracy. Our study demonstrates the feasibility of decision tree-based solutions as an accurate and lightweight approach for 3D image quality assessment.
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使用轻量级机器学习的全参考立体客观质量评估
几十年来对图像质量评估(IQA)的研究促进了各种与主观图像质量密切相关的客观质量指标的创建。然而,在考虑3D/立体图像的质量评估时,挑战仍然存在。通过对二维图像质量度量的扩展,设计了三维图像的多目标质量度量。因此,这些解决方案往往在3d特定工件下呈现弱点。最近的工作证明了机器学习技术在3D质量度量设计中的有效性。虽然有效,但一些基于机器学习的解决方案可能会导致高计算工作量,并限制其在低延迟轻量级系统/应用程序中的采用。提出了一种考虑轻量级机器学习的全参考立体客观质量评价方法。我们评估了四种不同的基于决策树的算法,考虑了八组不同的图像特征。分类器使用来自Waterloo IVC 3D图像质量数据库的数据进行训练,以确定使用平均意见分数(Mean Opinion score, MOS)测量的主观质量分数。结果表明,随机森林总体上获得了最好的准确率。我们的研究证明了基于决策树的解决方案作为一种准确和轻量级的3D图像质量评估方法的可行性。
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