Full Reference Stereoscopic Objective Quality Assessment using Lightweight Machine Learning

  • Narúsci Dos Santos Bastos UFPel
  • Lucas Seidy Ribeiro Dos Santos Ikenoue UFPel
  • Daniel Palomino UFPel
  • Guilherme Corrêa UFPel
  • Tatiana Tavares UFPel
  • Bruno Zatt UFPel


Decades of research on Image Quality Assessment (IQA) have pro moted 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 so lutions 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 compu tational 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 mea sured using Mean Opinion Score (MOS). The results show that Ran domForest generally obtains the best accuracy. Our study demon strates the feasibility of decision tree-based solutions as an accurate and lightweight approach for 3D image quality assessment.
Palavras-chave: 3D IQA, Machine Learning, Image Quality Assessment


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BASTOS, Narúsci Dos Santos; IKENOUE, Lucas Seidy Ribeiro Dos Santos; PALOMINO, Daniel; CORRÊA, Guilherme; TAVARES, Tatiana; ZATT, Bruno. Full Reference Stereoscopic Objective Quality Assessment using Lightweight Machine Learning. In: SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 301-310.

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