LFDD: Light field image dataset for performance evaluation of objective quality metrics

Adam Zizien; Karel Fliegel

doi:10.1117/12.2568490

21 August 2020 LFDD: Light field image dataset for performance evaluation of objective quality metrics

Adam Zizien, Karel Fliegel

Proceedings Volume 11510, Applications of Digital Image Processing XLIII; 115102U (2020) https://doi.org/10.1117/12.2568490
Event: SPIE Optical Engineering + Applications, 2020, Online Only

Conference Poster

Abstract

An increase in research activity around plenoptic content can be seen in recent years. As the communities around the different modalities grow, so does the demand for publicly available annotated datasets of suitable content. The datasets can be used for a multitude of purposes, such as to design novel compression algorithms, subjective evaluation methodologies or objective quality metrics. In this work, a new publicly available annotated light field image dataset is presented. The dataset consists of scenes corrupted by state-of-the-art image and video compression algorithms (JPEG, JPEG 2000, BPG, VP9, AV1, AVC, HEVC), noise, and geometric distortion. For the subjective evaluation of the included scenes, a modified version of the Double Stimulus Impairment Scale (DSIS) methodology was adopted. The views of each scene were organized into a pseudo-sequence and played to the observers as animations. The resulting subjective scores, together with additional data, are included in the dataset. The data can be used to evaluate the performance of currently used visual quality metrics as well as for the design of new ones.

Conference Presentation

Citation Download Citation

Adam Zizien and Karel Fliegel "LFDD: Light field image dataset for performance evaluation of objective quality metrics", Proc. SPIE 11510, Applications of Digital Image Processing XLIII, 115102U (21 August 2020); https://doi.org/10.1117/12.2568490

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