Multi-view videos plus depth assessment using novel saliency detection method

  • Authors

    • M. Sowjanya
    • B. Ramu
    • S. Krihna Priya
    https://doi.org/10.14419/ijet.v7i4.21599

  • Abstract

    Multi-view videos plus depth (MVD) is a popular 3D video representation where pixel depth information is exploited to generate additional views to provide 3D experience. Quality assessment of MVD data is of paramount importance since the latest research results show that existing 2D quality metrics are not suitable for MVD. This paper focuses on depth quality assessment and presents a novel algorithm to estimate the distortion in depth videos induced by compression. A novel saliency detection model is introduced by utilizing low level features obtained from Stationary Wavelet Transform domain. Firstly, wavelet transform is employed to create the multi-scale feature maps which can represent different features from edge to texture. Then, we propose a computational model for the saliency map from these features. This model is aimed to modulate local contrast at a location with its global saliency computed based on likelihood of the features and also considered local centre-surround differences and global contrast in the final saliency map. Experimental evaluation depicts the promising results from the proposed model by outperforming the relevant state of the art saliency detection models.

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  • How to Cite

    Sowjanya, M., Ramu, B., & Priya, S. K. (2018). Multi-view videos plus depth assessment using novel saliency detection method. International Journal of Engineering & Technology, 7(4), 3340-3345. https://doi.org/10.14419/ijet.v7i4.21599

    Received date: 2018-11-25

    Accepted date: 2018-11-25